Wrote about design of code
Signed-off-by: Jim Martens <github@2martens.de>
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body.tex
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@ -219,6 +219,34 @@ outlined, followed by the implementation of the auto-encoder.
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\section{Design of Source Code}
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The source code of many published papers is either not available
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or seems like an afterthought: it is poorly documented, difficult
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to integrate in your own work, and often does not follow common
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software development best practices. Moreover, with Tensorflow,
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PyTorch, and Caffe there are at least three machine learning
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frameworks. Every research team seems to prefer another framework
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and sometimes even develops their own; this makes it difficult
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to combine the work of different authors.
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In addition to all this, most papers do not contain proper information
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regarding the implementation details, making it difficult to
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accurately replicate them if their source code is not available.
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Therefore, it was clear to me: I will release my source code and
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make it available as Python package on the PyPi package index.
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This makes it possible for other researchers to simply install
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a package and use the API to interact with my code. Additionally,
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the code has been designed to be future proof and work with
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the announced Tensorflow 2.0 by supporting eager mode.
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Furthermore, it is configurable, well documented, and conforms
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to the clean code guidelines: evolvability and extendability among
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others. Unit tests are part of the code as well to identify common
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issues early on, saving time in the process.
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Lastly, the SSD implementation from a third party repository
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has been modified to work inside a Python package architecture and
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with Eager mode.
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\section{Preparation of data sets}
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\section{Replication of Miller et al.}
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