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Rectifier (neural networks)
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Rectifier (neural networks) : ウィキペディア英語版
Rectifier (neural networks)

In the context of artificial neural networks, the rectifier is an activation function defined as
:f(x) = \max(0, x)
where ''x'' is the input to a neuron. This is a ramp function. This activation function has been argued to be more biologically plausible
than the widely used logistic sigmoid (which is inspired by probability theory; see logistic regression) and its more practical
counterpart, the hyperbolic tangent. The rectifier is, , the most popular activation function for deep neural networks.
A unit employing the rectifier is also called a rectified linear unit (ReLU).〔
A smooth approximation to the rectifier is the analytic function
:f(x) = \ln(1 + e^x)
which is called the softplus function.〔C. Dugas, Y. Bengio, F. Bélisle, C. Nadeau, R. Garcia, NIPS'2000, (2001),(Incorporating Second-Order Functional Knowledge for Better Option Pricing )〕
The derivative of softplus is f'(x) = e^x / (e^x+1) = 1 / (1 + e^), i.e. the logistic function.
Rectified linear units find applications in computer vision using deep neural nets.〔
== Variants ==


抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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