Useful PyTorch Layers
Project description
Deep-Learning-Blocks
A library with customized PyTorch layers and model components.
What's available for use:
Layers:
- Funnel ReLU
- Flip-Invariant Conv2d
- Squeeze-Excitation Block
- Custom Multi-Head Self-Attention
Loss Functions:
- Focal Loss
- AUC Loss
- KL divergence Loss
Regularizer:
Self-supervised Learning:
Optimizers:
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
deepblocks-0.1.6.tar.gz
(19.5 kB
view hashes)
Built Distribution
deepblocks-0.1.6-py3-none-any.whl
(25.6 kB
view hashes)
Close
Hashes for deepblocks-0.1.6-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | df687b743471054126765fad05c4f58e3e7236ae7df7e07a7cc7ab87159fd9cc |
|
MD5 | f7767829443519aadffc6602dcdf6232 |
|
BLAKE2b-256 | b96054c45d7939118eeb4aad8b0f8706a91327b4ef6e0ebd03a2b129b7540262 |