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machine learning - What are the benefits of using ReLU over softplus as . . . Softplus is differentiable, which is an better in terms of training algorithms However, you might want to turn off some neurons and that can be advantageous, but it doesn't have to be However, you might want to turn off some neurons and that can be advantageous, but it doesn't have to be
machine learning - Why are the softmax, softplus, and softsign . . . Because Softmax, Softplus and Softsign have the smooth derivatives by normalization to stabilize convergence *They are continuously differentiable Softmax: can convert input values(xs) to the output values between 0 and 1 each and whose sum is 1(100%): *Memos: *0 and 1 are exclusive
Does it make sense to use `logit` or `softplus` loss for binary . . . On contrary, in the softplus loss $\mathcal{L_3} $, already right predictions will contribute less to the loss compared to wrong predictions $\mathcal{L_3} $ is actually equivalent to $\mathcal{L_1} $ The first hint is their gradients regarding the score are the same, thus their optimization dynamics are the same
machine learning - What are the benefits of using SoftPlus over ReLU . . . All the discussions online seem to be centered around the benefits of ReLU activations over SoftPlus The general consensus seems to be that the use of SoftPlus is discouraged since the computation of gradients is less efficient than it is for ReLU However, I have not found any discussions on the benefits of SoftPlus over ReLU
Soft Plus activation function with large values func SoftPlus(x float64) float64 { return (float64(1) (float64(1) + math Exp(-x))) } math Exp(-x) returns 0 or infinity with large values of x (actually + -1000 and greater lesser, if negative) The first solution which came to my mind is:
How does rectilinear activation function solve the vanishing gradient . . . One may hypothesize that the hard saturation at 0 may hurt optimization by blocking gradient back-propagation To evaluate the potential impact of this effect we also investigate the softplus activation: $ \text{softplus}(x) = \log(1 + e^x) $ (Dugas et al , 2001), a smooth version of the rectifying non-linearity We lose the exact sparsity, but