Ndezvipi zvakanakira kushandisa ReLU activation basa pamusoro pechinyakare sigmoid basa mune neural network?
1. Zvinhu zvose. Gradient vanishing problem: The max value of the derivative of sigmoid function is 0.25, which can result in the total gradients becoming very small during back propagation. Chinetso chekutsakatika kwegradient: Kukosha kukuru kwechinhu chinobva pabasa re sigmoid kuri 0.25, izvo zvinogona kukonzera kuti huwandu hwese hwegradients huve hudiki zvakanyanya panguva yekupararira kumashure. Kusiyana neizvi ReLU ine gradient isingachinji 1 nokuda kwezvinhu zvakanaka zvinopinda. 2. Kuziva Mwari. Compute kunyatsoshanda: ReLU anogona computed uchishandisa max ((0, x), zvikuru kunyatsoshanda pane exponential akavhiyiwa sigmoid basa. 3. Kuita zvinhu nenzira yakanaka. Convergence speed: zvinonziwo kushandisa ReLU se activation function muma neural networks kunowedzera nekukurumidza kudarika kushandisa sigmoid function.