The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various hand-designed alternatives to ReLU have been proposed, none have managed to replace it due to inconsistent gains. In this work, we propose to leverage automatic search techniques to discover new activation functions. Using a combination of exhaustive and reinforcement learning-based search, we discover multiple novel activation functions. We verify the effectiveness of the searches by conducting an empirical evaluation with the best discovered activation function. Our experiments show that the best discovered activation function, f(x) = x * sigmoid(beta * x), which we name Swish, tends to work better than ReLU on deeper models across a number of challenging datasets. For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9% for Mobile NASNet-A and 0.6% for Inception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network.
We use search techniques to discover novel activation functions, and our best discovered activation function, f(x) = x * sigmoid(beta * x), outperforms ReLU on a number of challenging tasks like ImageNet. Proposes a reinforcement learning based approach for finding non-linearity by searching through combinations from a set of unary and binary operators. This paper utilizes reinforcement learning to search the combination of a set of unary and binary functions resulting in a new activation function The author uses reinforcement learning to find new potential activation functions from a rich set of possible candidates. 