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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
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<html>
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<head>
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<meta name="GENERATOR" content="mkd2html 2.1.3 DL=DISCOUNT">
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<META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=utf-8">
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<link rel="stylesheet"
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type="text/css"
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href="../hint.css">
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<title>Most self deprecating</title>
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</head>
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<body>
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<h1>Most self deprecating</h1>
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<p>David Lowe<br>
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434 Tenney Drive<br>
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Rogue River, OR<br>
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97537<br>
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<a href="mailto:j.david.lowe@gmail.com">j.david.lowe@gmail.com</a><br>
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<a href="http://www.pootpoot.net/">http://www.pootpoot.net/</a></p>
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<h2>Judges' comments:</h2>
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<h3>To build:</h3>
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<pre><code>make dlowe
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</code></pre>
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<h3>To run:</h3>
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<pre><code>./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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</code></pre>
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<h3>Try:</h3>
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Download the
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<a href="/2011/dlowe/dlowe-aux-data.tar.bz2">2011 dlowe entry dlowe-aux-data.tar.bz2</a>
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file and then:
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<pre><code>
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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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</code></pre>
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<h3>Selected Judges Remarks:</h3>
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<p>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.</p>
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<h4>ioccc-winlose-trained.net</h4>
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<p>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.</p>
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<p>The ioccc-winlose-trained.net was trained using the earlystop.pl tool:</p>
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<pre><code>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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</code></pre>
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<p>where:</p>
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<pre><code>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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</code></pre>
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<p>The earlystop.pl tool wrote:</p>
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<pre><code>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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</code></pre>
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<p>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.</p>
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<p>The dlowe.c was explicitly excluded from this trailing set. So this test is interesting:</p>
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<pre><code>./dlowe dlowe.c < ioccc-winlose-trained.net
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</code></pre>
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<p>The match of dlowe.c to the lose/win trailed network was:</p>
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<pre><code>dlowe.c 0.125108
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</code></pre>
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<p>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!</p>
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<h4>other data sets to try</h4>
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<p>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:</p>
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<p>We created the english-trained.net as follows:</p>
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<pre><code>./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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</code></pre>
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<p>We created the png-trained.net as follows:</p>
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<pre><code>./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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</code></pre>
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<p>We created the xor-trained.net as follows:</p>
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<pre><code>./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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</code></pre>
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<h2>Author’s comments:</h2>
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<h1>Synopsis:</h1>
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<p>This is an artificially intelligent judging tool to help the IOCCC judges.
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Here’s to shorter, more frequent contests!</p>
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<h1>Description:</h1>
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<p>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.</p>
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<p>It has a fixed topology of 2<sup>16</sup> input neurons, 6 hidden neurons and 1 output
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neuron.</p>
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<p>The neurons' activation function is the logistic function 1 / (1 + e ^ -x).</p>
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<h2>Classifying</h2>
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<pre><code>./dlowe file [file ...] < trained.net
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</code></pre>
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<p>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.</p>
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<p>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 <em>probability</em> or a
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<em>confidence</em>.</p>
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<h2>Training</h2>
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<pre><code>./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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</code></pre>
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<p>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.</p>
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<p>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).</p>
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<p>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.</p>
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<p>The learning rate is hard-coded as 0.3. No momentum factor is used.</p>
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<h3>png corpora</h3>
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<p>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”.</p>
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<p>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”.</p>
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<p>Results:</p>
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<ul>
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<li>using these corpora as a training set</li>
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<li>using several hundred random <em>.gif and </em>.png files from my home computer as
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a test set</li>
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<li>using the included “earlystop.pl” script, which stopped after 67000 iterations.</li>
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<li>interpreting output of “> 0.5” as “probably a .png” and “< 0.5” as “probably
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a .gif”</li>
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<li>produced a network with about 87% accuracy</li>
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</ul>
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<h3>english corpora</h3>
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<p>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”.</p>
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<p>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”.</p>
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<p>Results:</p>
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<ul>
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<li>using these corpora as a training set</li>
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<li>using several dozen additional text files (obtained by the same methods)
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as a test set</li>
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<li>using the included “earlystop.pl” script, which stopped after 10000
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iterations.</li>
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<li>interpreting output of “> 0.5” as “probably english” and “< 0.5” as
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“probably french”</li>
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<li>produced a network with 100% accuracy</li>
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</ul>
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<h3>xor corpora</h3>
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<p>The xor-1 corpus consists of two files containing ‘01’ and ‘10’ respectively.</p>
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<p>The xor-0 corpus consists of two files containing ‘00’ and ‘11’ respectively.</p>
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<p>Results:</p>
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<ul>
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<li>using these corpora as a training set</li>
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<li>takes about 8000 iterations to learn xor to within a tolerance of <0.01</li>
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</ul>
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<h1>Limitations</h1>
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<p>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!)</p>
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<p>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…)</p>
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<p>Serialized network files are only portable between systems with the same
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floating-point representation and endianness.</p>
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<p>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.</p>
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<p>Bad input (e.g. nonexistent files, non-numeric number of iterations, etc.)
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tends to result in empty output.</p>
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<p>Given exactly one corpus, the program will crash or produce garbage.</p>
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<p>Leaks memory and file descriptors while processing files.</p>
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<p>Will crash and die horribly if it runs out of memory.</p>
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<p>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).</p>
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<p>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.</p>
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<h1>Obfuscation:</h1>
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<p>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 ;)</p>
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<p>Neural networks are interesting, and while the math isn’t terribly difficult,
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their behavior is difficult to fully understand.</p>
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<p>Had some extra space in one of the major data structures, and it seemed a
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shame to waste it.</p>
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<p>Similarly, I think three file pointers ought to be enough for anyone.</p>
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<p>Lots of magic numbers expressed in various ways.</p>
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<p>Random abuse of random C trivia.</p>
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<p>Some textual changes were made to maximize the classification of this program
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by a network trained to recognize ioccc winners ;)</p>
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<p>… but mostly zombies!</p>
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<hr />
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<TABLE><TR>
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<TD><a rel="license" href="http://creativecommons.org/licenses/by-sa/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-sa/3.0/88x31.png" /></a></TD>
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<TD><P>© Copyright 1984-2012,
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<A HREF="/judges.html">Leo Broukhis, Simon Cooper, Landon Curt Noll</A>
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- All rights reserved<BR>
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This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/3.0/">Creative Commons Attribution-ShareAlike 3.0 Unported License</a>.</P></TD>
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<TD> <!--<a href="http://validator.w3.org/check?uri=referer"><img src="http://www.w3.org/Icons/valid-html401" alt="Valid HTML 4.01 Transitional" height="31" width="88"></a>--></TD>
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</TR></TABLE>
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