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The inputs (features) and expected output for my ANN are these:

  • Input 1: Product id (number, cast to double)
  • Input 2: Year in the past (1900..2017, cast to double)
  • Input 3: Month of year (1..12, cast to double)
  • Expected output: Sale of month (number of units sold, cast to double)

I need to predict the sale of a product for a certain month in a certain year. How many layers and how many neurons on there layers should I put?

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    $\begingroup$ Figure it out through trial and error. You don't have a lot of inputs, so you should not make it to big. I think having two hidden layers is enough, with a maximum of 10 neurons per hidden layer. But what have you tried? Because you have such a small input/output it would be fairly easy to figure it out yourself through trial and error... $\endgroup$ May 9, 2017 at 9:33
  • $\begingroup$ i have 12 training samples for the 12 months of last year * 1 product. I use this shape [10,10,1], but the training doesn't converge after 30,000,000 sample iterations $\endgroup$
    – Dee
    May 9, 2017 at 9:43
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    $\begingroup$ Have you tried using an LSTM? $\endgroup$ May 9, 2017 at 9:48
  • $\begingroup$ never tried LSTM before, may be because of something wrong in my own ANN code, i'll try some libraries $\endgroup$
    – Dee
    May 9, 2017 at 9:58

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Every answer you get is just an opinion, it is based on experience. My answer is a single hidden layer of 5 neurons.

Besides, I recommend you to use a TWEANN system (Topology and Weight Evolving ANN).

Such system applys genetic algorithm to search and optimize an ANN with the optimal topology and weights.

Take a look at NEAT and DXNN.

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