線性整流函數

線性整流函數（rectifier），粵文又有叫修正線性單元，喺人工神經網絡上係指一種啟動函數（activation function），指個函數淨係接受正嘅數值：

f(x)=x^{+}=\max(0,x)

當中 $x$ 係粒人工神經細胞受到嘅輸入。線性整流函數係喺 2000 年始創嘅，目的係要為咗令人工神經網絡更加似生物神經網絡^[1]，而且喺 2010 年代初，研究證實咗線性整流函數（比起當時泛用嘅啟動函數）能夠提升人工神經網絡嘅表現^[2]，所以到咗 2017 年為止經已成為咗深度神經網絡最常用嘅啟動函數^[3]^[4]^[5]。

睇埋[編輯]

參考文獻[編輯]

Hahnloser, R.; Sarpeshkar, R.; Mahowald, M. A.; Douglas, R. J.; Seung, H. S. (2000). "Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit". Nature. 405 (6789): 947–951.

攷[編輯]

↑ Hahnloser, R.; Seung, H. S. (2001). Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks. NIPS 2001.
↑ Xavier Glorot, Antoine Bordes and Yoshua Bengio (2011). Deep sparse rectifier neural networks (PDF). AISTATS. Rectifier and softplus activation functions. The second one is a smooth version of the first."
↑ Yann LeCun, Leon Bottou, Genevieve B. Orr and Klaus-Robert Müller (1998). "Efficient BackProp" (PDF). In G. Orr and K. Müller (eds.). Neural Networks: Tricks of the Trade. Springer.
↑ LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (2015). "Deep learning". Nature. 521 (7553): 436–444.
↑ Ramachandran, Prajit; Barret, Zoph; Quoc, V. Le (October 16, 2017). "Searching for Activation Functions".

拎[編輯]

An Introduction to Deep Neural Networks.

[1] Hahnloser, R.; Seung, H. S. (2001). Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks. NIPS 2001.

[2] Xavier Glorot, Antoine Bordes and Yoshua Bengio (2011). Deep sparse rectifier neural networks (PDF). AISTATS. Rectifier and softplus activation functions. The second one is a smooth version of the first."

[3] Yann LeCun, Leon Bottou, Genevieve B. Orr and Klaus-Robert Müller (1998). "Efficient BackProp" (PDF). In G. Orr and K. Müller (eds.). Neural Networks: Tricks of the Trade. Springer.

[4] LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (2015). "Deep learning". Nature. 521 (7553): 436–444.

[5] Ramachandran, Prajit; Barret, Zoph; Quoc, V. Le (October 16, 2017). "Searching for Activation Functions".

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