Comprehensive Guide to the ReLU Activation Function in Neural Networks: Definition, Role, and Type Explained
ReLU stands for Rectified Linear Unit and is an activation function commonly used in artificial neural networks, especially in deep learning models. It's a simple but effective mathematical function that introduces non-linearity to the network's comp...
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In table 3, the function for Swish needs sigmoid(beta*x), unless beta is identically 1. From what I understand, beta is itself a trainable parameter, making it key to differentiate it from SiLU, where beta is identically 1.
Thus, the derivative should be: sigmoid(beta*x) plus beta times x times derivative_of_the_sigmoid function, where derivative of the sigmoid equals sigmoid(beta times x) times (1 minus sigmoid(beta times x))
Or if sigmoid is y, then derivative of y is equal to y times (1-y)