tensorflow / tensorflow/tensorboard
Faster, more performant t-SNE
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Description
Hi,
I am working on unsupervised clustering featuers vectors (length of 2048) of large sets of images (40k to 200k). I would like to do this using t-SNE and then explore it using the Tensorboard embedding visualizer. But the performance on Tensorboard is to poor for the task. This is probably because it is run on client side and I can only apply one cpu/gpu for it.
It would be nice if I could do t-SNE clustering and run a 1000 iterations offline (and use multi threaded processing) and then load the result in the Tensorboard embedding visualizer so I can explore and label it.
Looking forward to hear your ideas on this.
Greetings,
Nicolaas
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Research direction
The issue names no files, tests, or entry points. Start by reviewing TensorBoard's current client-side t-SNE flow and embedding visualizer, then determine how offline, 1000-iteration processing and multithreaded execution could integrate. Done would mean large image-vector sets can be processed offline and the results loaded for exploration and labeling.
Written by the indexing model from the issue text.
Assessment
- Domain
- data-visualization, machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100