Weight normalization layer for TensorFlow
Project description
tfwn
Weight Normalization layer wrapper for TensorFlow-Keras API.
Inspired by Sean Morgan implementation, but:
- No data initialization (only eager mode was implemented in original pull request).
- Code refactoring
- More tests
- CIFAR10 example from original paper reimplemented
Examples
Unfortunately I couldn't reproduce parer results on CIFAR10 with batch size 100. As you can see there is no much difference in accuracy.
But with much smaller batch size model with weight normalization is much better then regular one.
How to use
import tensorflow as tf
from tfwn import WeightNorm
dense_wn = WeightNorm(tf.keras.layers.Dense(3))
out = dense_wn(input)
References
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
Tim Salimans, and Diederik P. Kingma.
@inproceedings{Salimans2016WeightNorm,
title={Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks},
author={Tim Salimans and Diederik P. Kingma},
booktitle={Neural Information Processing Systems 2016},
year={2016}
}
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
tfwn-1.0.1.tar.gz
(5.0 kB
view hashes)