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.
The embedding project has been updated by Google to support new features, among which is the support for UMAP.
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-UMAP-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.