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Improved thesis based upon feedback

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
2019-10-04 13:43:27 +02:00
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appendix.tex
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@ -1,23 +1,23 @@
\chapter{Software and Source Code Design}
The source code of many published papers is either not available
or seems like an afterthought: it is poorly documented, difficult
or is of bad quality: it is poorly documented, difficult
to integrate into your own work, and often does not follow common
software development best practices. Moreover, with Tensorflow,
PyTorch, and Caffe there are at least three machine learning
frameworks. Every research team seems to prefer another framework
and sometimes even develops their own; this makes it difficult
frameworks. Every research team seems to prefer another framework,
and, occasionally, even develops their own; this makes it difficult
to combine the work of different authors.
In addition to all this, most papers do not contain proper information
regarding the implementation details, making it difficult to
accurately replicate them if their source code is not available.
In addition to this, most papers do not contain proper information
regarding implementation details, making it difficult to
accurately replicate their results, if their source code is not available.
Therefore, it was clear to me: I will release my source code and
make it available as Python package on the PyPi package index.
Therefore, I will release my source code and
make it available as a Python package on the PyPi package index.
This makes it possible for other researchers to simply install
a package and use the API to interact with my code. Additionally,
the code has been designed to be future proof and work with
the announced Tensorflow 2.0 by supporting eager mode.
the code has been designed to be future proof, and work with
the announced Tensorflow 2.0, by supporting eager mode.
Furthermore, it is configurable, well documented, and conforms largely
to the clean code guidelines: evolvability and extendability among
@ -38,7 +38,7 @@ can be found in plotting.py, and the ssd.py module contains
code to train the SSD and later predict with it.
Lastly, the SSD implementation from a third party repository
has been modified to work inside a Python package architecture and
has been modified to work inside a Python package architecture, and
with eager mode. It is stored as a Git submodule inside the package
repository.