  The activation function is at the heart of a deep neural networks
nonlinearity; the choice of the function has great impact on the success of
training. Currently, many practitioners prefer the Rectified Linear Unit (ReLU)
due to its simplicity and reliability, despite its few drawbacks. While most
previous functions proposed to supplant ReLU have been hand-designed, recent
work on learning the function during training has shown promising results. In
this paper we propose an adaptive piecewise linear activation function, the
Piecewise Linear Unit (PiLU), which can be learned independently for each
dimension of the neural network. We demonstrate how PiLU is a generalised
rectifier unit and note its similarities with the Adaptive Piecewise Linear
Units, namely adaptive and piecewise linear. Across a distribution of 30
experiments, we show that for the same model architecture, hyperparameters, and
pre-processing, PiLU significantly outperforms ReLU: reducing classification
error by 18.53% on CIFAR-10 and 13.13% on CIFAR-100, for a minor increase in
the number of neurons. Further work should be dedicated to exploring
generalised piecewise linear units, as well as verifying these results across
other challenging domains and larger problems.
