A Simple and easy to use way to Visualise Embeddings! Blog post on this project is here.
This project is forked from Tensorflow's Standalone Embedding Projector. It shows how a pretrained InceptionV3 model can be used on images and plotted in an interactive 3d map.
This project allows you to visualise any array of vectors with a light depency stack. It is designed to be decoupled from any library. Moreover , it uses a static file system, so you can publish your results without requiring a server. E.g. https://harveyslash.github.io/TSNE-Embedding-Visualisation/.
|-- data <-- where to put raw data
|-- Feature-extractor.ipynb <-- Demo of Embedding generation in a step by step fashion
|-- index.html <-- The GUI of the Viewer (Do not touch, unless you know what youre doing)
|-- LICENSE
|-- main.py <-- Executable to generate embedding data from command line args
|-- oss_data <-- required by the visualisation project
| |-- oss_demo_projector_config.json <-- all configuration files are stored here, this is modified by main.py automatically
| |-- sprites.png <-- sprites for the demo
| `-- tensor.bytes <-- embeddings array for the demo
`-- requirements.txt
This project requires python3.6. You can install all dependencies using pip install -r requirements.txt
Usage: main.py [OPTIONS]
Options:
--data TEXT Data folder,has to end with /
--name TEXT Name of visualisation
--sprite_size INTEGER Size of sprite
--tensor_name TEXT Name of Tensor file
--sprite_name TEXT Name of sprites file
--model_input_size INTEGER Size of inputs to model
--help Show this message and exit.
After you have run main.py, the sprites, tensors, and config.json should be updated. You can then serve the visualisation using a static file server. I just run python -m SimpleHTTPServer at the root level of the project. You can also upload the files to a github repository and then view it using github pages.
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