Inside EasyNN-plus

 
A Neural Network produced by EasyNN-plus has two component parts.
 
The component parts are the Node and the Connection.  These components are replicated to make the neural network.  A Node consists of a Neuron with positioning and connecting information. A Connection consists of a Weight with node addressing information.
 
The Grid used by EasyNN-plus also has two components.  These are the Example row and the Input/Output column.  These are replicated to make the grid.
 
 
All the component parts of EasyNN-plus are implemented as reusable classes to simplify future development.  The following information is a very basic description of the classes. The true names of the variables and functions are not used.
 
 
The Neural Network    
 
    Node    
 
          Positioning and connection
   
          Type:
              Input, Output or Hidden.
 
          Number:
              Node reference number.
 
          Layer Type:
              Input, Output or Hidden.
 
          First In:
              Number of the first connection into this node.
 
          Last In:
              Number of the last connection into this node.
 
          Neuron    
 
          Net Input:
              Sum of all activation * weight inputs to the node + Bias.
 
          Activation:
              1.0 / (1.0 + e (-Net Input))
 
          Output Node Error:
              Target - Activation
 
          Hidden Node Error:
              Error + Delta * Weight
 
          Delta:
              Error * Activation * (1.0 - Activation)
 
          Bias:
              Bias + Delta Bias
 
          Bias Derivative:
              Bias derivative + Delta
 
          Delta Bias:
              Learning Rate * Bias Derivative + Momentum * Delta Bias
 
    Connection    
 
          Node addressing    
 
          To:
              The Node that the connection is going to.
 
          From:
              Node that the connection is coming from.
 
          Number:
              Connection reference number.
 
    Weight    
 
          Type:
              Variable or Fixed.
 
          Weight:
              Weight + Delta Weight.
 
          Weight Derivative:
              Weight Derivative + To: Delta * From: Activation.
 
          Delta Weight:
              Learning Rate * Weight Derivative + Momentum * Delta Weight.
 
The Grid
   
    Example row
   
    Name:
          Optional Example name.
 
    Type:
          Training, Validating, Querying or Exclude.
 
    Values:
          Array of values in example row.
 
    Scaled Values:
          Array of scaled values in example row.
 
    Forecasted Values:
          Array of forecasted values in example row.
 
    Input/Output column
   
    Name:
          Optional Input/Output name.
 
    Type:
          Input, Output, Serial or Exclude.
 
    Mode:
          Real, Integer, Bool, Text or Image.
 
    Lock:
          True or False.
 
    Lowest:
          Lowest value in column.
 
    Highest:
          Highest value in column.
 
 
 
 

Created with help of DrExplain