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Format fix 2011/dlowe/README.md + update bugs.md

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2011/dlowe/README.md
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@@ -1,9 +1,6 @@
# Most self deprecating
David Lowe
434 Tenney Drive
Rogue River, OR
97537
<j.david.lowe@gmail.com>
## To build:
@@ -18,9 +15,9 @@ make
./dlowe -<n_iterations> corpus1/ [...] corpus0/ < start.net > trained.net
```
NOTE: In the above command, the directory args MUST end in a "/".
NOTE: In the above command, the directory args MUST end in a `/`.
Then to use the `trained.net`:
Then to use `trained.net`:
```sh
./dlowe file [file ...] < trained.net
@@ -46,7 +43,7 @@ Try these pre-trained networks:
You can run `./try.me.sh` to do the same thing.
NOTE: The `dlowe-aux-data.tar.bz2/` directory was created by:
NOTE: The `dlowe-aux-data/` directory was created by:
```sh
@@ -55,20 +52,21 @@ tar -jxf dlowe-aux-data.tar.bz2
## Judges' remarks:
This entry is a ghoulish example of a brain (dead?) neural network classifier.
It comes with a set of corpora for you to try.
NOTE: the author's website, http://www.pootpoot.net, no longer exists (as the
same) and the Internet Wayback Machine did not load scripts. Do you know if the
domain changed? Please provide us with the proper URL or if you know if it was
shut down for ever, please let us know that. Thank you!
This 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.net
### `ioccc-winlose-trained.net`
You 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](earlystop.pl) tool:
The `ioccc-winlose-trained.net` was trained using the
[earlystop.pl](earlystop.pl) tool:
```sh
rm -f ioccc-winlose-trained.net
@@ -78,11 +76,11 @@ rm -f ioccc-winlose-trained.net
where:
- `ioccc_won_training/`
1/2 of C code of the 20th IOCCC winners (except for dlowe.c from 2011)
1/2 of C code of the 20th IOCCC winners (except for [dlowe.c](dlowe.c) from 2011)
plus 1/2 of the C code winners from prior IOCCC contests
- `ioccc_won_test/`
1/2 of C code of the 20th IOCCC winners (except for dlowe.c from 2011)
1/2 of C code of the 20th IOCCC winners (except for [dlowe.c](dlowe.c) from 2011)
plus 1/2 of the C code winners from prior IOCCC contests
- `ioccc_lost_training/`
@@ -91,8 +89,8 @@ where:
- `ioccc_lost_test/`
1/2 of C code from entries that that did not win
NOTE: The above directories were not shipped and the C code from
entries that that did not win were flushed to /dev/null.
NOTE: The above directories were not shipped and everything from the entries
that did not win were flushed to `/dev/null`.
The [earlystop.pl](earlystop.pl) tool wrote:
@@ -100,7 +98,7 @@ The [earlystop.pl](earlystop.pl) tool wrote:
training 1001 to 2001...
prior error = 56.746974766043; current error = 58.095691135305
The resulting ioccc-winlose-trained.net file was included in the
The resulting `ioccc-winlose-trained.net` file was included in the
[dlowe-aux-data.tar.bz2](dlowe-aux-data.tar.bz2) tarball.
The [dlowe.c](dlowe.c) was explicitly excluded from this trailing set, so this
@@ -162,14 +160,15 @@ We created the [xor-trained.net](dlowe-aux-data/xor-trained.net) as follows:
- [dlowe-aux-data/xor-1/](dlowe-aux-data/xor-1/)
Data that XORs to 1
**NOTE**: The directory args **must end in a /** for them to work.
**NOTE**: The directory args **must end in a** `/` for them to work.
## Author's remarks:
### Synopsis
This is an artificially intelligent judging tool to help the IOCCC judges.
Here's to shorter, more frequent contests!
This is an [artificially
intelligent](https://en.wikipedia.org/wiki/Artificial_intelligence) judging tool
to help the IOCCC judges. Here's to shorter, more frequent contests!
### Description
@@ -206,7 +205,7 @@ trained, but it's geared toward interpretation as a *probability* or a
./dlowe -<n_iterations> corpus1/ [...] corpus0/ < start.net > end.net
```
**NOTE**: The directory args **must end in a /** for them to work.
**NOTE**: 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
@@ -220,55 +219,63 @@ 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 learning rate is hard-coded as `0.3`. No momentum factor is used.
#### png corpora
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-1](dlowe-aux-data/png-1) corpus was obtained by manually scraping the first results from an
<https://images.google.com> search for "[obfuscate
filetype:png](https://www.google.com/search?tbm=isch&sxsrf=APwXEddqIU34dxhpFdjEa8emNJKHOkYXIw:1683049158657&source=hp&biw=1727&bih=952&ei=xkpRZLapJtLKkPIP8bGKkAk&iflsig=AOEireoAAAAAZFFY1nbmEUHC74Cmg3Y5rEY1fV7ocASZ&ved=0ahUKEwi2pPXZltf-AhVSJUQIHfGYApIQ4dUDCAY&uact=5&oq=obfuscate+filetype:png&gs_lcp=CgNpbWcQA1AAWABg9QNoAHAAeACAATuIATuSAQExmAEAoAECoAEBqgELZ3dzLXdpei1pbWc&sclient=img&q=obfuscate&tbs=ift:png)".
The png-0 corpus was obtained by manually scraping the first results from an
https://images.google.com search for "obfuscate filetype:gif".
The [png-0](dlowe-aux-data/png-0) corpus was obtained by manually scraping the first results from an
<https://images.google.com> search for "[obfuscate
filetype:gif](https://www.google.com/search?tbm=isch&ved=2ahUKEwiowo7fltf-AhXSOUQIHbJBDp8Q2-cCegQIABAA&oq=obfuscate+filetype:gif&gs_lcp=CgNpbWcQAzoECCMQJzoHCAAQigUQQzoFCAAQgARQ6AxY6AxgpBJoAHAAeACAAUeIAcsBkgEBM5gBAKABAaoBC2d3cy13aXotaW1nwAEB&sclient=img&ei=0UpRZOiVINLzkPIPsoO5-Ak&bih=952&biw=1727&q=obfuscate&tbs=ift:gif)".
Results:
##### Results:
* using these corpora as a training set.
* using several hundred random `*.gif` and `*.png` files from my home computer as
a test set
* using the included [earlystop.pl](earlystop.pl) script, which stopped after 67000 iterations.
* interpreting output of `> 0.5` as "probably a .png" and `< 0.5` as "probably
a .gif"
* produced a network with about 87% accuracy
* Using these corpora as a training set;
* Using several hundred random `*.gif` and `*.png` files from my home computer as
a test set;
* Using the included [earlystop.pl](earlystop.pl) script, which stopped after 67000 iterations.
* Interpreting output of `> 0.5` as "probably a `.png`" and `< 0.5` as "probably
a `.gif`"
produced a network with about 87% accuracy
#### English corpora
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-1](dlowe-aux-data/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](https://www.google.com/search?q=paris+filetype%3Atxt&lr=lang_en&tbs=lr%3Alang_1en&sxsrf=APwXEdeHXyUYdUxCYQQDHmJhbHzX9aARSA%3A1683049834430&source=hp&ei=ak1RZJHUGN_DkPIP26OtkA8&iflsig=AOEireoAAAAAZFFbejA0Xfqoq__FXx--Ok0S6GQjnGnA&ved=0ahUKEwiRsZOcmdf-AhXfIUQIHdtRC_IQ4dUDCAo&uact=5&oq=paris+filetype%3Atxt&gs_lcp=Cgdnd3Mtd2l6EANQAFgAYN4DaABwAHgAgAE-iAE-kgEBMZgBAKABAqABAQ&sclient=gws-wiz)".
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".
The [english-0](dlowe-aux-data/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](https://www.google.com/search?q=paris+filetype%3Atxt&lr=lang_fr&tbs=lr%3Alang_1fr&sxsrf=APwXEddcsQ-gMhrqSUJo6omWFUl7jQMASg%3A1683049870736&source=hp&ei=jk1RZLevK_fKkPIPjbWN-AU&iflsig=AOEireoAAAAAZFFbnvExHGkcavB5H8F0xsLAWN2KfYFK&ved=0ahUKEwi3rrutmdf-AhV3JUQIHY1aA18Q4dUDCAo&uact=5&oq=paris+filetype%3Atxt&gs_lcp=Cgdnd3Mtd2l6EANQAFgAYLMDaABwAHgAgAE-iAE-kgEBMZgBAKABAqABAQ&sclient=gws-wiz)".
##### Results:
* using these corpora as a training set.
* using several dozen additional text files (obtained by the same methods)
as a test set.
* using the included [earlystop.pl](earlystop.pl) script, which stopped after 10000
iterations.
* interpreting output of `> 0.5` as "probably English" and `< 0.5` as
"probably French"
* produced a network with 100% accuracy
* Using these corpora as a training set;
* Using several dozen additional text files (obtained by the same methods) as a
test set;
* Using the included [earlystop.pl](earlystop.pl) script, which stopped after
10000 iterations;
* Interpreting output of `> 0.5` as "probably English" and `< 0.5` as "probably
French"
produced a network with 100% accuracy.
#### xor corpora
The xor-1 corpus consists of two files containing '01' and '10' respectively.
The [xor-1](dlowe-aux-data/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.
The [xor-0](dlowe-aux-data/xor-0) corpus consists of two files containing `00`
and `11` respectively.
##### Results:
* using these corpora as a training set.
* takes about 8000 iterations to learn xor to within a tolerance of `<0.01`.
Using these corpora as a training set takes about 8000 iterations to learn xor
to within a tolerance of `<0.01`.
### Limitations
@@ -277,12 +284,14 @@ bytes in them (I'm looking at you, [smr.c](/1994/smr/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...)
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.
[floating-point](https://en.wikipedia.org/wiki/Floating-point_arithmetic)
representation and [endianness](https://en.wikipedia.org/wiki/Endianness).
Making sure not to overfit the network to the training data is a bit of a
Making sure not to
[overfit](https://en.wikipedia.org/wiki/Overfitting#Machine_learning) the network to the training data is a bit of a
black art. I have enclosed [earlystop.pl](earlystop.pl), a wrapper script that implements
a simple 'early stopping' algorithm; other techniques are possible.
@@ -295,8 +304,8 @@ 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
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,
@@ -304,7 +313,8 @@ you'll eventually have a training session that completely fails to converge.
### Obfuscation
- Zombies! (Since neural networks are modeled after BRAINS, ya know? And
- [Zombies](https://en.wikipedia.org/wiki/Zombie)! (Since neural networks are
modeled after [BRAINS](https://en.wikipedia.org/wiki/Brain), ya know? And
corpus sounds a lot like corpse. And I have 4- and 7-year-old kids ;) )
- Neural networks are interesting, and while the math isn't terribly difficult,
their behavior is difficult to fully understand.
@@ -314,7 +324,7 @@ shame to waste it.
- Lots of magic numbers expressed in various ways.
- Random abuse of random C trivia.
- Some textual changes were made to maximize the classification of this program
by a network trained to recognize ioccc winners ;)
by a network trained to recognize [IOCCC winners](/winners.html) ;)
... but mostly zombies!
bugs.md
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@@ -1849,6 +1849,20 @@ instead being something else entirely. The Internet Wayback Machine, although it
archived it, did not load scripts. Do you know if the domain was moved? Do you
have an archive or mirror? Please provide us it! Thank you.
## STATUS: INABIAF - please **DO NOT** fix
The author states the following:
* 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.
## [2011/richards](2011/richards/richards.c) ([README.md](2011/richards/README.md))
## STATUS: doesn't work with some platforms - please help us fix