#Swish activation function free#

They tune deep neural network architectures to achieve an optimal balance between accuracy and performance, meanwhile tailored for mobile and resource limited environments. Lightweight neural networks, also known as mobile neural networks, are specially designed for realizing real-time visual information processing. IET Generation, Transmission & Distribution.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology.It comes with its own problem though: the usage of the exponential function makes it relatively slow to compute. It takes the best of all worlds: non-zero gradients for negative values eliminate the dying neurons problem just like in leaky ReLU, negative values make the outputs closer to zero just like in tanh, and most importantly, ELU is smooth around zero, which speeds up convergence. Some exotic variants of this activation function include the randomized leaky ReLU (RReLU), in which this small slope is chosen randomly while training, or the parametrized leaky ReLU (PReLU), in which the slope is considered one of the network’s parameters and is learned via gradient descent.įinally, the exponential linear unit (ELU) came to be, beating all the relly variants. Leaky ReLU has a small but non-zero slope for negative values that ensures the neurons won’t die. To alleviate the dying ReLU problem, a couple of upgrades to the ReLU have been proposed. If many neurons die like this, the networks learning capabilities get impaired. If the network’s weights reach such values that they always yield negative values when multiplied with the inputs, then the entire ReLU-activated unit keeps producing zeros. The problem is that ReLU outputs zeros for any negative value. It has one drawback though, referred to as dying ReLU. It is fast to compute, and thanks to the lack of a maximum value, it prevents the vanishing gradient problem. A success story belongs to the r ectified linear unit (ReLU) function, which does not saturate for positive values. Hence the need for non-saturating activations. When the input is very large or very small, the slope approaches zero, making the gradients vanish and the learning slow. As a result, its outputs are more centered around zero, which helps speed up convergence, especially early in the training.īoth the sigmoid and the tanh, however, share one issue: they are saturating functions. The hyperbolic tangent (tanh) is quite similar in shape to the sigmoid, but it takes values between -1 and 1, instead of between 0 and 1. Since then, the sigmoid has been displaced by other functions inside the networks’ hidden layers, although it is still used as the final predictive layer for binary classification tasks. It was a gamechanger, as the sigmoid’s well-defined, non-zero derivative allowed for the usage of gradient descent to train the neural networks. According to science, this is roughly the function that is used to activate neurons in our biological brains. The sigmoid or logistic activation was historically the first one to replace step-functions in early networks.

#Swish activation function free#

If you can tell between PReLU and RReLU, feel free to scroll down past the first two sections.Ĭlassical activation function: plots.

But before we do, we will quickly go through the commonly used activations to understand what problems do they solve or create. Let’s take a look at a couple of the most promising ones to see why are they good and when to use them.

I have recently browsed through some relatively recent papers on one of the building blocks of neural networks: the activation functions. What a shame it would have been to miss some golden nugget! Dread not, for I got you covered. Most of these new inventions never make it to become default go-to methods, sometimes because they prove not as good as it was initially hoped, but sometimes simply because they end up crumpled in the flood of new publications. Every now and then, a new paper pops up claiming to have achieved some state-of-the-art results. It is quite hard to stay up-to-date, unless only within a narrow niche. The pace of scientific progress in the field of machine learning is unparalleled these days. Should you “swish” to these new inventions, or stay with the oldies but goldies?