Smooth ReLU in TensorFlow

Unofficial TensorFlow reimplementation of the Smooth ReLU (SmeLU) activation function proposed in the paper Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations by Gil I. Shamir and Dong Lin.

This repository includes an easy-to-use pure TensorFlow implementation of the Smooth ReLU.

Installation

The SmeLU can be installed by using pip.

pip install git+https://github.com/r3v1/tf-SmeLU

Example Usage

import tensorflow as tf
from tf_smelu import smelu

x = tf.range(-6, 6, 1, dtype=float)  # <tf.Tensor: numpy=array([-6., -5., -4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.,  5.], dtype=float32)>

smelu(x, beta=0.1)  # <tf.Tensor: numpy=array([0.,0.,0.,0.,0.,0.,0.025,1.,2.,3.,4.,5.], dtype=float32)>
smelu(x, beta=0.5)  # <tf.Tensor: numpy=array([0.,0.,0.,0.,0.,0.,0.125,1.,2.,3.,4.,5.], dtype=float32)>
smelu(x, beta=1.)   # <tf.Tensor: numpy=array([0.,0.,0.,0.,0.,0.,0.25 ,1.,2.,3.,4.,5.], dtype=float32)>

The SmeLU takes the following parameters.

• beta: Half-width of a symmetric transition region around x = 0. Defaults to 1.

Reference

@article{Shamir2022,
        title={{Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations}},
        author={Shamir, Gil I and Lin, Dong},
        journal={{arXiv preprint arXiv:2202.06499}},
        year={2022}
}