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Activation functions are essential for deep learning methods to learn andperform complex tasks such as image classification. Rectified Linear Unit(ReLU) has been widely used and become the default activation function acrossthe deep learning community since 2012. Although ReLU has been popular,however, the hard zero property of the ReLU has heavily hindered the negativevalues from propagating through the network. Consequently, the deep neuralnetwork has not been benefited from the negative representations. In this work,an activation function called Flatten-T Swish (FTS) that leverage the benefitof the negative values is proposed. To verify its performance, this studyevaluates FTS with ReLU and several recent activation functions. Eachactivation function is trained using MNIST dataset on five different deep fullyconnected 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 thebest overall performance. As compared with ReLU, FTS (T=-0.20) improves MNISTclassification accuracy by 0.13%, 0.70%, 0.67%, 1.07% and 1.15% on wider 5layers, 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 asReLU. Although there are other existing activation functions are alsoevaluated, this study elects ReLU as the baseline activation function.

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