Rename hint.txt and hint.text to README.md
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# Most self deprecating
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David Lowe
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434 Tenney Drive
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Rogue River, OR
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97537
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<j.david.lowe@gmail.com>
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<http://www.pootpoot.net/>
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## Judges' comments:
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### To build:
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make dlowe
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### To run:
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./dlowe -<n_iterations> corpus1/ [...] corpus0/ < start.net > end.net
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NOTE: The directory entries MUST end in a /
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./dlowe file [file ...] < trained.net
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### Try:
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Download the dlowe-aux-data.tar.bz2 file from:
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curl -O http://www.ioccc.org/2011/dlowe/dlowe-aux-data.tar.bz2
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tar -jxf dlowe-aux-data.tar.bz2
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./dlowe dlowe.c < ioccc-winlose-trained.net
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./dlowe dlowe < ioccc-winlose-trained.net
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./dlowe xor-0/00 < xor-trained.net
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./dlowe xor-0/11 < xor-trained.net
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./dlowe xor-1/01 < xor-trained.net
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./dlowe xor-1/10 < xor-trained.net
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./dlowe hint.html < english-trained.net
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./dlowe dlowe-aux-data.tar.bz2 < png-trained.net
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### Selected Judges Remarks:
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This entry is a ghoulish example of a brain (dead?) neural network classifier.
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It comes with a set of corpora for you to try.
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#### ioccc-winlose-trained.net
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You can create your own trained networks. For example, we trained a network
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on the C code of entries that were supplied to the 20Th IOCCC.
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The ioccc-winlose-trained.net was trained using the earlystop.pl tool:
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rm -f ioccc-winlose-trained.net
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./earlystop.pl ioccc-winlose-trained.net ioccc_won_training/ ioccc_lost_training/ ioccc_won_test/ ioccc_lost_test/
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where:
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ioccc_won_training/
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1/2 of C code of the 20th IOCCC winners (except for dlowe.c from 2011)
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plus 1/2 of the C code winners from prior IOCCC contests
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ioccc_won_test/
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1/2 of C code of the 20th IOCCC winners (except for dlowe.c from 2011)
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plus 1/2 of the C code winners from prior IOCCC contests
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ioccc_lost_training/
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1/2 of C code from entries that that did not win
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ioccc_lost_test/
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1/2 of C code from entries that that did not win
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NOTE: The above directies were not shipped and the C code from
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entries that that did not win was flushed to /dev/null.
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The earlystop.pl tool wrote:
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training 1 to 1001...
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training 1001 to 2001...
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prior error = 56.746974766043; current error = 58.095691135305
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The resulting ioccc-winlose-trained.net file was included in the
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dlowe-aux-data.tar.bz2 file in the above mentioned URL.
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The dlowe.c was explicitly excluded from this trailing set. So this test is interesting:
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./dlowe dlowe.c < ioccc-winlose-trained.net
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The match of dlowe.c to the lose/win trailed network was:
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dlowe.c 0.125108
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NOTE: The non-artificially intelligent decisions of the IOCCC judges include random
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quirks and their non-linear judgment calls not found in the ioccc trained network.
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Just because C code is or isn't scored highly by this network does not
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mean it will win or lose a future IOCCC. On the other hand ... it just might!
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#### other data sets to try
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While we cannot supply you with the losing IOCCC source code, the author did supply a number
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data sets on which to test:
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We created the english-trained.net as follows:
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./dlowe -8000 english-1/ english-0/ < /dev/null > english-trained.net
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english-0/
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Non-English (French) text
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english-1/
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English (non-French) text
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We created the png-trained.net as follows:
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./dlowe -8000 png-1/ png-0/ < /dev/null > png-trained.net
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png-0/
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Non-png (gif) images
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png-1/
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png (non-gif) images
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We created the xor-trained.net as follows:
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./dlowe -8000 xor-1/ xor-0/ < /dev/null > xor-trained.net
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xor-0/
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Data that XORs to 0
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xor-1/
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Data that XORs to 1
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## Author's comments:
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# Synopsis:
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This is an artificially intelligent judging tool to help the IOCCC judges.
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Here's to shorter, more frequent contests!
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# Description:
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This is a multilayer perceptron (a feedforward artificial neural network)
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which can be trained, using on-line backpropagation, to classify input files.
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It has a fixed topology of 2^16 input neurons, 6 hidden neurons and 1 output
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neuron.
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The neurons' activation function is the logistic function 1 / (1 + e ^ -x).
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## Classifying
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./dlowe file [file ...] < trained.net
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To classify files, one specifies a trained network (on stdin) and one or more
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files to classify. The program will output one line per successfully-classified
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file to stderr, with the filename and the classification: a number between 0
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and 1.
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The interpretation of the classification number depends on how the network was
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trained, but it's geared toward interpretation as a *probability* or a
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*confidence*.
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## Training
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./dlowe -<n_iterations> corpus1/ [...] corpus0/ < start.net > end.net
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NOTE: The directory args must end in a / for them to work.
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To train a network, one specifies a starting network (on stdin), two or more
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corpora (directories containing training data), and the number of training
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iterations to run. The program will write some progress data to stderr and,
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when it's done, will serialize the updated network to stdout.
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If no input network is given, a random new network will be generated as a
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starting point (i.e. provide an empty stdin).
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The first corpus will be assigned a target value of 1. The last will be
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assigned a target value of 0. Intervening directories (if any) will be assigned
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intermediate target values.
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The learning rate is hard-coded as 0.3. No momentum factor is used.
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### png corpora
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The png-1 corpus was obtained by manually scraping the first results from an
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https://images.google.com search for "obfuscate filetype:png".
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The png-0 corpus was obtained by manually scraping the first results from an
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https://images.google.com search for "obfuscate filetype:gif".
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Results:
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* using these corpora as a training set
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* using several hundred random *.gif and *.png files from my home computer as
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a test set
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* using the included "earlystop.pl" script, which stopped after 67000 iterations.
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* interpreting output of "> 0.5" as "probably a .png" and "< 0.5" as "probably
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a .gif"
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* produced a network with about 87% accuracy
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### english corpora
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The english-1 corpus was obtained by manually scraping the first results from
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a https://www.google.com/webhp?lr=lang_en search for "paris filetype:txt".
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The english-0 corpus was obtained by manually scraping the first results from
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a https://www.google.com/webhp?lr=lang_fr search for "paris filetype:txt".
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Results:
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* using these corpora as a training set
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* using several dozen additional text files (obtained by the same methods)
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as a test set
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* using the included "earlystop.pl" script, which stopped after 10000
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iterations.
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* interpreting output of "> 0.5" as "probably english" and "< 0.5" as
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"probably french"
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* produced a network with 100% accuracy
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### xor corpora
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The xor-1 corpus consists of two files containing '01' and '10' respectively.
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The xor-0 corpus consists of two files containing '00' and '11' respectively.
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Results:
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* using these corpora as a training set
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* takes about 8000 iterations to learn xor to within a tolerance of <0.01
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# Limitations
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The program can't tell you anything meaningful about files with less than two
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bytes in them (I'm looking at you, smr.c!)
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You must include the trailing directory separator on training directories
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(this allows the program to be portable without wasting precious bytes on
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figuring out how to concatenate directory and file names...)
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Serialized network files are only portable between systems with the same
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floating-point representation and endianness.
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Making sure not to overfit the network to the training data is a bit of a
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black art. I have enclosed 'earlystop.pl', a wrapper script that implements
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a simple 'early stopping' algorithm; other techniques are possible.
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Bad input (e.g. nonexistent files, non-numeric number of iterations, etc.)
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tends to result in empty output.
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Given exactly one corpus, the program will crash or produce garbage.
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Leaks memory and file descriptors while processing files.
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Will crash and die horribly if it runs out of memory.
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The Microsoft C compiler doesn't provide a dirent API, so to get this working
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on a Windows system you'll need cygwin+gcc (tested) or a dirent compatibility
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library (untested, but they do exist).
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Backpropagation doesn't always converge: if you play with this long enough,
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you'll eventually have a training session that completely fails to converge.
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# Obfuscation:
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Zombies! (Since neural networks are modeled after BRAINS, ya know? And
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corpus sounds a lot like corpse. And I have 4- and 7-year-old kids ;)
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Neural networks are interesting, and while the math isn't terribly difficult,
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their behavior is difficult to fully understand.
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Had some extra space in one of the major data structures, and it seemed a
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shame to waste it.
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Similarly, I think three file pointers ought to be enough for anyone.
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Lots of magic numbers expressed in various ways.
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Random abuse of random C trivia.
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Some textual changes were made to maximize the classification of this program
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by a network trained to recognize ioccc winners ;)
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... but mostly zombies!
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--------------------------------------------------------------------------------
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<!--
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(c) Copyright 1984-2012, [Leo Broukhis, Simon Cooper, Landon Curt Noll][judges] - All rights reserved
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This work is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License][cc].
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[judges]: http://www.ioccc.org/judges.html
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[cc]: http://creativecommons.org/licenses/by-sa/3.0/
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-->
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