makeThe current status of this entry is:
STATUS: missing or dead link - please provide them
STATUS: INABIAF - please **DO NOT** fixFor more detailed information see 2011 dlowe bugs.
./dlowe -<n_iterations> corpus1/ [...] corpus0/ < start.net > trained.netNOTE: In the above command, the directory args MUST end in a /.
Then to use trained.net:
./dlowe file [file ...] < trained.netTry these pre-trained networks:
./try.shNOTE: The dlowe-aux-data/ directory, which try.sh refers to, was
created by:
tar -jxf dlowe-aux-data.tar.bz2This entry is a ghoulish example of a brain (dead?) neural network classifier. It comes with a set of corpora for you to try.
ioccc-winlose-trained.netYou can create your own trained networks. For example, we trained a network on the C code of entries that were supplied to the 20Th IOCCC.
The ioccc-winlose-trained.net was trained using the
earlystop.pl tool:
rm -f ioccc-winlose-trained.net
./earlystop.pl ioccc-winlose-trained.net ioccc_won_training/ ioccc_lost_training/ ioccc_won_test/ ioccc_lost_test/where:
ioccc_won_training/
1/2 of C code of the 20th IOCCC entries (except for dlowe.c from 2011)
plus 1/2 of the C code winning entries from prior IOCCC contests
ioccc_won_test/
1/2 of C code of the 20th IOCCC entries (except for dlowe.c from 2011)
plus 1/2 of the C code winning entries from prior IOCCC contests
ioccc_lost_training/
1/2 of C code from entries that that did not win
ioccc_lost_test/
1/2 of C code from entries that that did not win
NOTE: The above directories were not shipped and everything from the entries
that did not win were flushed to /dev/null.
The earlystop.pl tool wrote:
training 1 to 1001...
training 1001 to 2001...
prior error = 56.746974766043; current error = 58.095691135305The resulting ioccc-winlose-trained.net file was included in the
dlowe-aux-data.tar.bz2 tarball.
The dlowe.c was explicitly excluded from this trailing set, so this test is interesting:
./dlowe dlowe.c < dlowe-aux-data/ioccc-winlose-trained.netNOTE: this is done in try.sh.
The match of dlowe.c to the lose/win trailed network was:
dlowe.c 0.125108NOTE: The non-artificially intelligent decisions of the IOCCC judges include random quirks and their non-linear judgment calls not found in the ioccc trained network. Just because C code is or isn’t scored highly by this network does not mean it will win or lose a future IOCCC. On the other hand … it just might!
While we cannot supply you with the losing IOCCC source code, the author did supply a number data sets on which to test:
We created the english-trained.net as follows:
./dlowe -8000 dlowe-aux-data/english-1/ dlowe-aux-data/english-0/ < /dev/null > dlowe-aux-data/english-trained.netdlowe-aux-data/english-0/ Non-English (French) text
dlowe-aux-data/english-1/ English (non-French) text
We created the png-trained.net as follows:
./dlowe -8000 png-1/ png-0/ < /dev/null > dlowe-aux-data/png-trained.netdlowe-aux-data/png-0/ Non-png (gif) images
dlowe-aux-data/png-1/ png (non-gif) images
We created the xor-trained.net as follows:
./dlowe -8000 dlowe-aux-data/xor-1/ dlowe-aux-data/xor-0/ < /dev/null > dlowe-aux-data/xor-trained.netdlowe-aux-data/xor-0 Data that XORs to 0
dlowe-aux-data/xor-1/ Data that XORs to 1
NOTE: The directory args must end in a / for them to work.
This is an artificially intelligent judging tool to help the IOCCC judges. Here’s to shorter, more frequent contests!
This is a multilayer perceptron (a feedforward artificial neural network) which can be trained, using on-line backpropagation, to classify input files.
It has a fixed topology of 2^16 input neurons, 6 hidden neurons and 1 output
neuron.
The neurons’ activation function is the logistic function 1 / (1 + e ^ -x).
./dlowe file [file ...] < trained.netTo classify files, one specifies a trained network (on stdin) and one or more
files to classify. The program will output one line per successfully-classified
file to stderr, with the filename and the classification: a number between 0
and 1.
The interpretation of the classification number depends on how the network was trained, but it’s geared toward interpretation as a probability or a confidence.
./dlowe -<n_iterations> corpus1/ [...] corpus0/ < start.net > end.netNOTE: The directory args must end in a / for them to work.
To train a network, one specifies a starting network (on stdin), two or more
corpora (directories containing training data), and the number of training
iterations to run. The program will write some progress data to stderr and,
when it’s done, will serialize the updated network to stdout.
If no input network is given, a random new network will be generated as a
starting point (i.e. provide an empty stdin).
The first corpus will be assigned a target value of 1. The last will be
assigned a target value of 0. Intervening directories (if any) will be assigned
intermediate target values.
The learning rate is hard-coded as 0.3. No momentum factor is used.
The png-1 corpus was obtained by manually scraping the first results from an https://images.google.com search for “obfuscate filetype:png”.
The png-0 corpus was obtained by manually scraping the first results from an https://images.google.com search for “obfuscate filetype:gif”.
*.gif and *.png files from my home computer as
a test set;> 0.5 as “probably a .png” and < 0.5 as “probably
a .gif”produced a network with about 87% accuracy
The english-1 corpus was obtained by manually scraping the first results from a https://www.google.com/webhp?lr=lang_en search for “paris filetype:txt”.
The english-0 corpus was obtained by manually scraping the first results from a https://www.google.com/webhp?lr=lang_fr search for “paris filetype:txt”.
> 0.5 as “probably English” and < 0.5 as “probably
French”produced a network with 100% accuracy.
The xor-1 corpus consists of two files containing 01
and 10 respectively.
The xor-0 corpus consists of two files containing 00
and 11 respectively.
Using these corpora as a training set takes about 8000 iterations to learn xor
to within a tolerance of <0.01.
The program can’t tell you anything meaningful about files with less than two bytes in them (I’m looking at you, smr.c!)
You must include the trailing directory separator on training directories (this allows the program to be portable without wasting precious bytes on figuring out how to concatenate directory and file names…).
Serialized network files are only portable between systems with the same floating-point representation and endianness.
Making sure not to overfit the network to the training data is a bit of a black art. I have enclosed earlystop.pl, a wrapper script that implements a simple ‘early stopping’ algorithm; other techniques are possible.
Bad input (e.g. nonexistent files, non-numeric number of iterations, etc.) tends to result in empty output.
Given exactly one corpus, the program will crash or produce garbage.
Leaks memory and file descriptors while processing files.
Will crash and die horribly if it runs out of memory.
The Microsoft C compiler doesn’t provide a dirent API, so to get this working
on a Windows system you’ll need cygwin+gcc (tested) or a dirent compatibility
library (untested, but they do exist).
Backpropagation doesn’t always converge: if you play with this long enough, you’ll eventually have a training session that completely fails to converge.
… but mostly zombies!
![dlowe-aux-data/png-1/image_thumb[40].png](dlowe-aux-data/png-1/image_thumb%5B40%5D.png){kind=link}
