Body: <p>The inputs (features) and expected output for my ANN are these:</p>
  
  <ul>
  <li>Input 1: Product id (number, cast to double)</li>
  <li>Input 2: Year in the past (1900..2017, cast to double)</li>
  <li>Input 3: Month of year (1..12, cast to double)</li>
  <li>Expected output: Sale of month (number of units sold, cast to double)</li>
  </ul>
  
  <p>I need to predict the sale of a product for a certain month in a certain year. <strong>How many layers and how many neurons on there layers should I put</strong>?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-05-09T09:33:11.667Z
Comments.Text: 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...
Comments.UserDisplayName: Thomas Wagenaar
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-05-09T09:43:26.880Z
Comments.Text: 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
Comments.UserDisplayName: Dan D.
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-05-09T09:48:02.103Z
Comments.Text: Have you tried using an LSTM?
Comments.UserDisplayName: Thomas Wagenaar
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-05-09T09:58:14.083Z
Comments.Text: never tried LSTM before, may be because of something wrong in my own ANN code, i'll try some libraries
Comments.UserDisplayName: Dan D.
Tags: <neural-networks><ai-design><prediction><hidden-layers>
Title: How many layers and neurons on the layers does a neural network need for for sales prediction?
UserDisplayName: Dan D.
ThreadText: # How many layers and neurons on the layers does a neural network need for for sales prediction?
  
  (Asked by: username_0 on 2017-05-09)
  
  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**?
  
  **Tags:** neural-networks, ai-design, prediction, hidden-layers
  
  > **Comments:**
  > **username_1**: 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...  
  > **username_0**: 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  
  > **username_1**: Have you tried using an LSTM?  
  > **username_0**: never tried LSTM before, may be because of something wrong in my own ANN code, i'll try some libraries  
  
  ---
  
  ## 0 answers
  
  