Filos92:

I would like to learn my network with data we collected in a test mode. (For example: A picture and data A, B, C, D). Now the network is trained and I leave the test mode. In the actual operation, however, only the data A, C and D are available. Do I have to thin it out before or can I replace the Inputs with Zeros? will the accuracy of the system suffer? Is it even possible to use the network? If Yes, How?


Hi,
In your case, you have missing data in the testing phase. There are scenarios where the missing data can be approximated using ideas based on distance. For example, you can use nearest neighbor to fill the missing data.  Or else, you can fill with the column average. Read the following article, it will give you a good idea to handle the missing data.

  
      
      Towards Data Science – 31 Jan 18
  
  
    

How to Handle Missing Data 6

“The idea of imputation is both seductive and dangerous” (R.J.A Little & D.B. Rubin)

  Reading time: 8 min read
    

  
  
    
    
  
  


Just replacing the missing values with zeros may affect your accuracy. Analyse the particular column data and use suitable replacement for the missing values properly. To use the network, you need all values A,B,C,D. Hence, you need some approximation. Yes, the accuracy will go down if you do not have a good method to handle missing data.
Thanks