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"readMe: Description of Neural Network Notebooks \nNotebook Version: 2.0\n\
Steve Hunka, University of Alberta\nsteve.hunka@ualberta.ca\nMod. Date: Oct \
31/97 V3.0 revision",
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"\n\nThere are 9 Notebooks in this collection. Two Notebooks provide \
documentation, and seven form a collection of functions for running a simple \
back propagated neural network and analyzing its dynamic and final state \
characteristics. These Notebooks are as follows:\n\n1. ",
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"; holds functions for classifying exemplars (usually not in the training \
set) and provides bar charts representing the output from the hidden layer \
cells and output layer cells.\n3. ",
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"; graphical representations of the error sqd at each output cell, and \
responses from the hidden and output layer cells. Uses data from each \
iteration for the error plot, and from each epoch for the plot of responses.\n\
4. ",
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"; graphical representations of weights at the hidden and output layer \
cells. Uses data from each epoch for plotting.\n5. ",
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"; graphical representations of hidden and output cell responses using bar \
plots (as in netCls.nb) and density plots showing input attributes together \
with hidden cell and output cell data at last epoch.\n6. ",
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"; provides functions for carrying out a singular value decomposition of \
hidden cell response data as well as 2D and 3D plots. Last epoch data only \
are used.\n7. ",
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"; is a Package containing the most commonly used functions for the 6 \
Notebooks listed above. The user must do a save of this notebook to produce \
the Package netUtil.m required for running with V3.0.\n\nThe Notebook ",
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" contains documentation for the functions in netUtil.nb.\n \
\nEach Notebook contains documentation for those functions in the \
Notebook. Neural.nb also contains some test data referred to as the \"TCL\" \
problem. The TCL problem is a modification of the example problem J.A. \
Freeman illustrates in his textbook Simulating Neural Networks with ",
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" , which requires the network to discriminate between the characters T and \
C in different orientations, and to which the character L has been added.\n\n\
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" the following changes occurred or were explicitly made:\n1) Comments \
were removed by the conversion process. Some, but not all comments were \
replaced.\n2) FileNames[fname} now returns file names prepended with a colon. \
Functions rcvrExpr, rprtFdata and getFdata in netUtil were modifided to \
accommodate this change.\n3) The plotting function ScatterPlot3D in V3.0 \
returns a directive GrayLevel[0] not in V2.2 resulting in incorrect indexes \
in modification of the graphic object by function splt3d in Notebooks netAn1 \
and netAn2. Indexing was modified for V3.0.\n4) If the result of $RandomState \
saved by option in running the simulation function bpnn in Notebook Neural is \
done using V2.2 it cannot be used in V3.0. Thus, a simulation run in V2.2 \
cannot be recreated in V3.0, and for this reason the numerical values \
obtained by using V3.0 are different than those using V2.0 although \
acceptable convergence was obtained in both cases.(A preferred method may be \
to ask the user for a seed n, saving this value, and then use SeedRandom[n] \
to recreate a simulation.) \n5) The function mplotLists in Notebook netAn2 \
in V2.2 removed the plotting symbol by manipulating the graphic object \
produced by MultipleListPlot. The option SymbolShape->None is now used.\n\n",
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" version 2.2 on a Macintosh 6100/60 and 7200/120, and then revised for \
Version 3.0.1.1x on a Macintosh 7200/120. The larger sized runs of the \
Neural.nb functions have used 1144 input cells, 12 hidden cells, and 18 \
output cells. \n\n",
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by the user to produce netUtil.m so that its functions can be loaded in the \
Notebooks. Some Notebooks require additional Packages supplied by ",
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", and in this case these are documented in each Notebook. Normally, one \
would use Neural.nb first, and then depending upon the type of analysis \
wanted of either the dynamic or final state characteristics of the network, \
use the other Notebooks. \n\nThe Notebook netUtil.nb, although written as a ",
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" since the user is not normally expected to call the functions directly, \
except in the case of the functions rcvrExpr, rprtFdata, and saveExpr. Since \
almost all functions are written for interactive user input of parameters, \
checking of arguments is made before the functions in netUtil are called. \
netUdoc.nb contains documentation of the functions in netUtil.nb. \n\n",
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"\n\nResults of computations displayed to the user's screen may be more \
extensive than usually expected. This has been done to provide the user with \
additional information confirming that, for example, calculations appear \
reasonable, and that the appropriate data file is being used. \n\n",
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"\n\nUser options are provided for writing a number of data sets to a file \
during the running of Neural.nb : (a) saving of the result of $RandomState, \
(b) saving the squared error at each iteration, (c) saving the responses and \
weights at the hidden and output layer at each epoch, and (d) saving the \
random seed and last epoch data. When data is extensive, it is much faster to \
obtain last epoch data from (d) than from (c). Functions in Notebook netAn1 \
require error and epoch data, i.e., items (b and c). Functions in Notebook \
netAn2 requires item (c). Functions in netAn3 and netAn4 require data of the \
last epoch. Writing to files as in (c) adds to the run time of the \
simulation. (Hidden and output cell weights for the last epoch are also \
returned by the function bpnn in Neural.nb). The user may wish to set a \
specific file path for files written.\n\n",
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"\n\nExperience suggests that users must keep accurate records of the files \
they create if data are to be recovered reliably at some future date. \
Keeping a log of the runID, date time stamps, and associated file names is \
important together with information about the simulation such as the nature \
of the exemplars, number of input, hidden, and output cells. (runID is a user \
input in the function bpnn in Neural.nb, but the date time stamp is generated \
automatically and displayed.) If the user saves a number of ",
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file (using the function saveExpr), the function rprtFdata can be used to \
provide a summary of the file contents. \n\n\n",
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"\n\nChecking Parameter Integrity: The interactive keyboard input functions \
check for the structure of the input, but there are many combinations of \
options that a user might attempt to use. It is likely all possible \
combinations have not been tested. Obvious illogical input errors are not \
checked, e.g., 0 input cells, 0 hidden cells, and 0 output cells.\n\nExemplar \
Presentation Order: The graphical analysis Notebooks assume that a fixed \
order (not random order) of exemplars has been used in Neural.nb. \n\nAmount \
of Data Generated: The amount of data generated and which could be saved in \
files by Neural.nb could be very extensive depending upon the number of \
iterations, epochs, and number of cells used. For example, having thousands \
of input cells, run for 200 or 300 epochs with just a few exemplars could \
generate a hidden cell weight file of 20MB or more. Any graphical analysis \
of the dynamic characteristics of the network requiring plotting from such a \
large file, would require extensive amounts of RAM allocated to the ",
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the final state data of the network is used, however, being congnizant of the \
number of plots which may result is important. \n\n\n",
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