Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learning

TL;DR

This paper introduces Flatten-T Swish, a new activation function that incorporates negative values to improve deep neural network performance, demonstrating better accuracy and faster convergence than ReLU on MNIST classification tasks.

Contribution

The paper proposes Flatten-T Swish, a novel activation function that leverages negative values, and empirically evaluates its superior performance over ReLU in deep neural networks.

Findings

FTS with T=-0.20 achieves the best overall performance.

FTS improves MNIST accuracy by up to 1.15% over ReLU.

FTS converges twice as fast as ReLU.

Abstract

Activation functions are essential for deep learning methods to learn and perform complex tasks such as image classification. Rectified Linear Unit (ReLU) has been widely used and become the default activation function across the deep learning community since 2012. Although ReLU has been popular, however, the hard zero property of the ReLU has heavily hindered the negative values from propagating through the network. Consequently, the deep neural network has not been benefited from the negative representations. In this work, an activation function called Flatten-T Swish (FTS) that leverage the benefit of the negative values is proposed. To verify its performance, this study evaluates FTS with ReLU and several recent activation functions. Each activation function is trained using MNIST dataset on five different deep fully connected neural networks (DFNNs) with depth vary from five to eight layers. For a fair evaluation, all DFNNs are using the same configuration settings. Based on the experimental results, FTS with a threshold value, T=-0.20 has the best overall performance. As compared with ReLU, FTS (T=-0.20) improves MNIST classification accuracy by 0.13%, 0.70%, 0.67%, 1.07% and 1.15% on wider 5 layers, slimmer 5 layers, 6 layers, 7 layers and 8 layers DFNNs respectively. Apart from this, the study also noticed that FTS converges twice as fast as ReLU. Although there are other existing activation functions are also evaluated, this study elects ReLU as the baseline activation function.