Deep-Learning-Blocks
A library with customized PyTorch layers and model components.
Install:
To install the latest stable version:
pip install deepblocks
For a specific version:
pip install deepblocks==0.1.9
To install the latest, but unstable version:
pip install git+https://github.com/blurry-mood/Deep-Learning-Blocks
What's available:
Networks
- ConvMixer
- U-Net
- ICT-Net
Layers
- ConvMixer Layer
- Flip-Invariant Conv2d
- Squeeze-Excitation Block
- Dense Block
- Multi-Head Self-Attention
- Multi-Head Self-Attention V2
Activations
- Funnel ReLU
Loss Functions
- Focal Loss
- AUC Loss
- AUC Margin Loss
- KL Divergence Loss
Regularization functions
- Anti-Correlation
Self-supervised Learning
- Barlow Twin
- DINO
Optimizers
- SAM
Documentation:
The current documention is hosted here
Bug or Feature:
Deepblocks is a growing package. If you encounter a bug or would like to request a feature, please feel free to open an issue here.
1 maintainer
Metadata
1 maintainer
Release files for deepblocks 0.1.13
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| File | Size | Uploaded | Provenance | |
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| deepblocks-0.1.13.tar.gz | 21.9 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| deepblocks-0.1.13-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 53.6 kB
Release files / deepblocks-0.1.13.tar.gz
| Download URL | deepblocks-0.1.13.tar.gz |
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Release files / deepblocks-0.1.13-py3-none-any.whl
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| Tags | Python 3 |
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