nn_vis
A project for processing neural networks and rendering to gain insights on the architecture and parameters of a model through a decluttered representation. (by julrog)
WeightVis
Visualize neural network weights from different kind of libraries (by frknayk)
nn_vis | WeightVis | |
---|---|---|
3 | 1 | |
1,045 | 13 | |
- | - | |
5.9 | 10.0 | |
4 months ago | over 3 years ago | |
Python | Python | |
MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nn_vis
Posts with mentions or reviews of nn_vis.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-05-11.
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[D] Is there a tool to visualise my neural network in real time?
In my master Thesis I did some work on visualizing a neural network. It is not trivial to show the weights in a meaningful/understandable way. https://github.com/julrog/nn_vis You need a portion of training data and it's just for fully connected layers currently. The visualization is 3D and in realtime, but it needs some preprocessing, so i'm not sure if it fits your needs at all.
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Finding important connections
I had some some success on pruning weights with adding batch normalization layer between existing layer, freezing the existing layer and then and retrain the model with the batch normalization layer (training can be much shorter because of way less weights to train). Then using magnitude of the original weights with the weights from the batch normalization, you can prune the original model. You can see an example for fully connected layer in my code: https://github.com/julrog/nn_vis
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[D] Convolution Neural Network Visualization - Made with Unity 3D and lots of Code / source - stefsietz (IG)
I just made my project public on GitHub, which seems similar to yours https://github.com/julrog/nn_vis
WeightVis
Posts with mentions or reviews of WeightVis.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-05-11.
-
[D] Is there a tool to visualise my neural network in real time?
You can try our tool https://github.com/frknayk/WeightVis
What are some alternatives?
When comparing nn_vis and WeightVis you can also consider the following projects:
DeepFaceLab - DeepFaceLab is the leading software for creating deepfakes.
pyomyo - PyoMyo - Python Opensource Myo armband library
nn-visualizer
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OpenXR-SDK-Source - Sources for OpenXR loader, basic API layers, and example code.
EyeTrackVR - Open Source and Affordable, Virtual Reality Eye Tracking Platform.
rf2_video_settings - Create presets of your rFactor 2 settings and quickly change between performance focused VR setup or an eye-candy favoured Replay setup.
Longhand - Text corpora in virtual reality