ReLU is the max function(x,0) with input x e.g. matrix from a convolved image. ReLU then sets all negative values in the matrix x to zero and all other values are kept constant. ReLU is computed after the convolution and is a nonlinear activation function like tanh or sigmoid. Softmax is a classifier at the end of the neural network.

(2) The exact zero values of relu for z<0 introduce sparsity effect in the network, which forces the network to learn more robust features. If this is true, something like leaky Relu, which is claimed as an improvement over relu, may be actually damaging the efficacy of Relu. Some people consider relu very strange at first glance.

The earliest usage of the ReLU activation that I've found is Fukushima (1975, page 124, equation 2). Thanks to johann to pointing this out. Fukushima also wrote at least one other paper involving ReLU activations (1980), but this is the earliest one that I am aware of.

2021年5月24日 · Hornik at least mentions at page 253 in the bottom, that their theorem does not account for all unbounded activation functions. The behavior of an unbounded tail function is also highlighted. So I would "guess" ReLu falls into tha category, where this theorem can not account for all unbounded activation functions $\endgroup$ –

2024年1月29日 · 从ReLU函数及其表达式可以看出，ReLu其实就是一个取最大值的函数。 在输入是负值的情况下，其输出为0，表示神经元没有被激活。 这意味着在网络的前向传播过程中，只有部分神经元被激活，从而使得网络很稀疏，进而提高运算效率。

2016年6月14日 · ReLU. Use the ReLU non-linearity, be careful with your learning rates and possibly monitor the fraction of “dead” units in a network. If this concerns you, give Leaky ReLU or Maxout a try. Never use sigmoid. Try tanh, but expect it to work worse than ReLU/Maxout.

2015年10月14日 · RELU has gradient 1 when output > 0, and zero otherwise. Hence multiplying a bunch of RELU derivatives together in the backprop equations has the nice property of being either 1 or zero - the update is either nothing, or takes …

2015年4月13日 · This is a motivation behind leaky ReLU, and ELU activations, both of which have non-zero gradient almost everywhere. Leaky ReLU is a piecewise linear function, just as for ReLU, so quick to compute. ELU has the advantage over softplus and ReLU that its mean output is closer to zero, which improves learning.

Variance calculation RELU function (deep learning) Ask Question Asked 10 years, 1 month ago.

一, relu的优点主要有两方面: relu(z) = max(0, z) 一是, 在加权和>0时, 导数恒定为1, 不会造成梯度饱和, 所谓梯度饱和就是函数的输出有上限, 随着输入的增加而输出增速放缓, 较小的增速意味着较小的梯度, 这也是造成梯度消失的根源.