Holds the output from each layer of the neural network. This is very useful for the propagation algorithms that need to examine the output of each individual layer.
NeuralOutputHolder Class Documentation
Esempio n. 1
0
 /// <summary>
 /// Compute the output for a given input to the neural network. This method
 /// provides a parameter to specify an output holder to use.  This holder
 /// allows propagation training to track the output from each layer.
 /// If you do not need this holder pass null, or use the other
 /// compare method.
 /// </summary>
 /// <param name="input">The input provide to the neural network.</param>
 /// <param name="useHolder">Allows a holder to be specified, this allows
 /// propagation training to check the output of each layer.</param>
 /// <returns>The results from the output neurons.</returns>
 public virtual INeuralData Compute(INeuralData input,
                                    NeuralOutputHolder useHolder)
 {
     try
     {
         return(logic.Compute(input, useHolder));
     }
     catch (IndexOutOfRangeException ex)
     {
         throw new NeuralNetworkError(
                   "Index exception: there was likely a mismatch between layer sizes, or the size of the input presented to the network.",
                   ex);
     }
 }
Esempio n. 2
0
 /// <summary>
 /// Compute the output for a given input to the neural network. This method
 /// provides a parameter to specify an output holder to use.  This holder
 /// allows propagation training to track the output from each layer.
 /// If you do not need this holder pass null, or use the other 
 /// compare method.
 /// </summary>
 /// <param name="input">The input provide to the neural network.</param>
 /// <param name="useHolder">Allows a holder to be specified, this allows
 /// propagation training to check the output of each layer.</param>
 /// <returns>The results from the output neurons.</returns>
 public virtual INeuralData Compute(INeuralData input,
          NeuralOutputHolder useHolder)
 {
     try
     {
         return logic.Compute(input, useHolder);
     }
     catch (IndexOutOfRangeException ex)
     {
         throw new NeuralNetworkError(
                 "Index exception: there was likely a mismatch between layer sizes, or the size of the input presented to the network.",
                 ex);
     }
 }