https://github.com/pmixer/zan.initializer

Sample to show how input dim(input_dim for FC layer, for CNN, it's prod(tensor.shape[1:])) reciprocal could be used for initialize NN weights rather than sqrt(input_dim_reciprocal) hinted by Xavier:dragon:
https://github.com/pmixer/zan.initializer

cnn initial-nn-weights tensor xavier

Last synced: 4 months ago
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Sample to show how input dim(input_dim for FC layer, for CNN, it's prod(tensor.shape[1:])) reciprocal could be used for initialize NN weights rather than sqrt(input_dim_reciprocal) hinted by Xavier:dragon:

• Host: GitHub
• URL: https://github.com/pmixer/zan.initializer
• Owner: pmixer
• Created: 2020-08-12T04:16:44.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-08-12T04:26:55.000Z (almost 5 years ago)
• Last Synced: 2025-01-07T18:31:13.929Z (5 months ago)
• Topics: cnn, initial-nn-weights, tensor, xavier
• Language: Jupyter Notebook
• Homepage:
• Size: 67.4 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# zan.initializer

Sample to show how input dim(input_dim for FC layer, for CNN, it's prod(tensor.shape[1:])) reciprocal could be used for initial NN weights:dragon:

Sometimes more effective than xavier initializer in my experiments if without BatchNorm layers inside the network.

```

@misc{zan2017initializer,

  title={Input Dimension Reciprocal for Effective Weight Initialization},

  author={Huang, Zan},

  year={2017},

  publisher={GitHub},

  howpublished={\url{https://github.com/pmixer/zan.initializer}},

}

```