(''[RS] would call this '''A little feed-forward back-propagation learning neural network'''.'')

[http://tcl.jtang.org/ffbp/ffbp-xor.png]

There came a time when I needed to do a series of simulations of a feed-forward back-propagaion (FFBP) neural network[http://en.wikipedia.org/wiki/Neural_network].  Rather then spend just an hour doing the homework, I instead spent over 10 hours writing a full-fledged network builder and simulator.  As you can see from the screenshot above the result is a little tool that allows the user to graphically draw a network and then run simulations upon it.

Features include:
   * hidden and bias nodes
   * choice of the sigmoid (or logistic function[http://en.wikipedia.org/wiki/Sigmoid_function]) or bipolar squashing function
   * configurable eta value (learning rate)
   * learning by way of steepest descent and delta rule[http://en.wikipedia.org/wiki/Delta_rule]

The bipolar function is g(x)=2f(x)-1, where f(x) is the sigmoid function.  Conveniently, its derivative is g'(x)=0.5(1+g(x))(1-g(x)).  A more general-purpose steepest descent algorithm may be found at [Differentiation and steepest-descent], though my FFBP does not use his code.

'''Usage'''

Shift-click the canvas to place a node.  Shift-drag between nodes to add a weight link.  Reposition nodes by dragging them around.  Double click a node or weight to change its properties.

The real power of the program is its ability to run a number of trials and to learn after each test datum.  To do this requires modifying the code a bit.  See the last three functions in ffbp.tcl.  The current functions demonstrate how to learn the XOR function; the corresponding network is saved in '''ffbp.net'''.

While you are at it, take a look at another kind of neural network, [Hopfield Networks].

To do list:
   * add momentum value
   * better interface for running trials

'''Downloads'''

Version 0.2 - http://tcl.jtang.org/ffbp/ffbp-0.2.tar.gz
   * ''This version breaks save file compatibility with previous version!''
   * Remove nodes and weights by double-clicking them, then clicking on 'Remove' button.
   * Show current weights directly on the canvas.
   * Adjust weight format using [format] codes; set string to empty to see raw value.

Version 0.1 - http://tcl.jtang.org/ffbp/ffbp-0.1.tar.gz
   * Initial hack-ish release.

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See also [ANN]
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[Category Mathematics] | [Artificial Intelligence with Tcl]