https://github.com/benkeene/ntk_utils
Tools for the analysis of ANNs.
Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
○DOI references
-
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (4.1%) to scientific vocabulary
Last synced: 10 months ago
·
JSON representation
Repository
Tools for the analysis of ANNs.
Basic Info
- Host: GitHub
- Owner: benkeene
- Language: Python
- Default Branch: master
- Size: 23.5 MB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Fork of ajacot/NTK_utils
Created about 3 years ago
· Last pushed about 3 years ago
https://github.com/benkeene/NTK_utils/blob/master/
# NTK_utils The file network.py contains an alternative definition of the usual Linear and ConvNd layers using the parametrization described in the article https://arxiv.org/abs/1806.07572. This parametrization gives a consistent scaling behaviour as one increases the width of the network (the number of neurons in the hidden layers). As a result of the reparametrization, we suggest a learning rate around 1.0. Furthermore network.py contains a module LinearNet which defines a fully-connected network given a list of the number of neurons in each layer. From this module, one can directly calculate the Neural Tangent Kernel, and the activation kernels as described in the article.
Owner
- Name: Benjamin Keene
- Login: benkeene
- Kind: user
- Repositories: 1
- Profile: https://github.com/benkeene
Mathematics PhD student at UCF