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LP Extractor Protocol (view source)
Revision as of 13:47, 21 September 2020
, 13:47, 21 September 2020no edit summary
{{Project
|Has project output=Tool
|Has sponsor=Kauffman Incubator Project
|Has title=LP Extractor Protocol
|Has owner=Lasya Rajan,
* [https://github.com/ngundotra/code2pix code2pix]
: A project to recreate an inverse architecture to pix2code, with the objective of creating a GAN (Generative Adversarial Network) to replace pix2code.
=== DFS Encoding ===
: Github repo that contains reference implementation of node2vec algorithm as a python module. See above node2vec paper.
: A [https://github.com/thunlp/OpenNE toolkit] containing node2vec implemented in a framework based on tensorflow
: [https://towardsdatascience.com/node2vec-embeddings-for-graph-data-32a866340fef Here] is a very good and elementary introduction to node2vec
* [https://networkx.github.io/documentation/stable/index.html NetworkX]
: NetworkX is a Python package for loading, visualizing, and processing graph data. Includes built in functions for DFS encoding, and constructing adjacency and edges to vertices matrices.
* [http://www.numpy.org/ NumPy]
: NumPy is a computing package that includes a N-dimensional array object (useful in encoding) and many other functions to process data. Is required for pix2code.
=== DSL Development ===
* [http://hackage.haskell.org/package/lucid Lucid]
: Lucid is a DSL implemented with Haskell for writing HTML. It represents DOM elements as functions, and uses specific notation to differentiate between data elements and code elements.
=== General ===
* [https://www.h5py.org/ H5PY]
: The h5py package can be used to store large amounts of numerical data, and integrates well with NumPy
=== Useful tutorials ===
: Since we will be using a two-layer LSTMs in tensorflow, this [https://medium.com/@erikhallstrm/using-the-tensorflow-multilayered-lstm-api-f6e7da7bbe40 article] might be useful.
=== Proposed Model ===
: Here is a visualization of the model that we might want to use for our extractor
[[File: Extractor-Model.png| first diagram of extractor model]]
==DSL Encoder==
To encode the structure of the DSL scripts, we can try using one-hot vector. More details can be found [https://blog.floydhub.com/turning-design-mockups-into-code-with-deep-learning/ here] and on the [[DSL Encoding]] page.
