kernelinitializer: Initializer for the kernel weights matrix. usebias: Boolean, whether the layer uses a bias vector. The simplicity of Swish and its similarity to ReLU make itĮasy for practitioners to replace ReLUs with Swish units in any neural network. If you don't specify anything, no activation is applied (ie. For example, simply replacing ReLUs with Swish units improves top-1Ĭlassification accuracy on ImageNet by 0.9\% for Mobile NASNet-A and 0.6\% for To work better than ReLU on deeper models across a number of challengingĭatasets. Our experiments show that the best discovered activationįunction, $f(x) = x \cdot \text(\beta x)$, which we name Swish, tends This dict maps the activation function name from its v2 version to its canonical name. The searches by conducting an empirical evaluation with the best discoveredĪctivation function. Using a combination of exhaustive and reinforcement learning-based search, weĭiscover multiple novel activation functions. To leverage automatic search techniques to discover new activation functions. At the same time, it means that you can use any activation function name as a string. So, activation'elu' will be converted into tf. (). Have managed to replace it due to inconsistent gains. When keras receives tf. (10, activation'elu') it will go into the activation function module and will literally call activation function by its name if it is present there. Currently, the most successfulĪnd widely-used activation function is the Rectified Linear Unit (ReLU).Īlthough various hand-designed alternatives to ReLU have been proposed, none On the training dynamics and task performance.
Swish activation function keras pdf#
Download a PDF of the paper titled Searching for Activation Functions, by Prajit Ramachandran and 2 other authors Download PDF Abstract: The choice of activation functions in deep networks has a significant effect
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