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Almathera Ten Pack 3: CDPD 3
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Almathera Ten on Ten - Disc 3: CDPD3.iso
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ab20
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ab20_archive
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utilities
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misc
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neural-network-2.lha
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neural_network_v2.readme
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1992-03-27
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This package contains all the functions necessary to generate a
Neural network which you can train and use in your programs. The
Neural network has an input layer, two hidden layers, and an ouput layer.
The network is feedforward and fully connected. You may specify an size for
each layer at run-time. This code is Public Domain and anyone can use it in
any type of program.
This is version 2 of the C++ Neural network code. The only modifications
made were changing from <stdio.h> I/O to <iostream.h> I/O. This allows
the code to be compiled under GCC2. Also I removed the dependency on the
File class from libg++. The code still has a dependency on a String class
but it can be removed by changing the prototypes of the following three
functions:
Neural_network (String& filename, ...);
read_weights (String& filename, ...);
save_weights (String& filename, ...);
Just change String& to char * in Neural_network.h and Neural_network.cc and
recompile. I only made them String& because GCC2 seems to have trouble
passing a String as a char * reliably. The variables 'filename' are used
as char *'s in each function.
The following files are included in this package:
C++ files
Neural_network.cc --> C++ Neural_network class code
Neural_network.h --> C++ Neural_network header file
xor_dbd.cc --> Example of how to solve the XOR problem using the
delta bar delta rule.
xor_bp.cc --> Example of how to solve the XOR problem using
straight back propagation.
weights.xor --> Initialized weights that are read in by the XOR programs
because the gcc,gpp rand () function generates a new
sequence of random numbers everytime the program is
run. This file makes sure every XOR training session
starts at the exact same place so you can see how
changing learning parameters affects the rate of
convergence.
Document files
Readme_V2.1st --> This file
Neural_network.doc --> Explanation of each Neural network function.
I would like to here from anyone using this code on its performance or any
improvements or options that should be added.
You can email me at
anstey@sun.soe.clarkson.edu