Translations:Neural Networks/18/zh: Difference between revisions

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    ([deploy-bot] Translate Neural Networks unit 18 to zh)
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    {| class="wikitable"
    {| class="wikitable"
    |-
    |-
    ! 函数 !! 公式 !! 取值范围 !! 备注
    ! 函数 !! 公式 !! 范围 !! 备注
    |-
    |-
    | '''Sigmoid''' || <math>\sigma(z) = \frac{1}{1+e^{-z}}</math> || (0, 1) || 历史上常用；存在梯度消失问题
    | '''Sigmoid''' || <math>\sigma(z) = \frac{1}{1+e^{-z}}</math> || (0, 1) || 历史上很流行；存在梯度消失问题
    |-
    |-
    | '''Tanh''' || <math>\tanh(z) = \frac{e^z - e^{-z}}{e^z + e^{-z}}</math> || (−1, 1) || 以零为中心；对大输入仍会饱和
    | '''Tanh''' || <math>\tanh(z) = \frac{e^z - e^{-z}}{e^z + e^{-z}}</math> || (−1, 1) || 以零为中心；对大输入仍会饱和
    |-
    |-
    | '''ReLU''' || <math>\max(0, z)</math> || [0, ∞) || 现代网络的默认选择；可能导致"死亡神经元"
    | '''ReLU''' || <math>\max(0, z)</math> || [0, ∞) || 现代网络中的默认选择；可能导致"死神经元"
    |-
    |-
    | '''Leaky ReLU''' || <math>\max(\alpha z, z)</math>，其中 <math>\alpha > 0</math> 较小 || (−∞, ∞) || 缓解死亡神经元问题
    | '''Leaky ReLU''' || <math>\max(\alpha z, z)</math>，其中 <math>\alpha > 0</math> 较小 || (−∞, ∞) || 解决死神经元问题
    |-
    |-
    | '''Softmax''' || <math>\frac{e^{z_i}}{\sum_j e^{z_j}}</math> || (0, 1) || 用于多分类输出层
    | '''Softmax''' || <math>\frac{e^{z_i}}{\sum_j e^{z_j}}</math> || (0, 1) || 用于多分类输出层
    |}
    |}

    Revision as of 03:35, 27 April 2026

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    Message definition (Neural Networks)
    {| class="wikitable"
    |-
    ! Function !! Formula !! Range !! Notes
    |-
    | '''Sigmoid''' || <math>\sigma(z) = \frac{1}{1+e^{-z}}</math> || (0, 1) || Historically popular; suffers from vanishing gradients
    |-
    | '''Tanh''' || <math>\tanh(z) = \frac{e^z - e^{-z}}{e^z + e^{-z}}</math> || (−1, 1) || Zero-centred; still saturates for large inputs
    |-
    | '''ReLU''' || <math>\max(0, z)</math> || [0, ∞) || Default choice in modern networks; can cause "dead neurons"
    |-
    | '''Leaky ReLU''' || <math>\max(\alpha z, z)</math> for small <math>\alpha > 0</math> || (−∞, ∞) || Addresses the dead-neuron problem
    |-
    | '''{{Term|softmax}}''' || <math>\frac{e^{z_i}}{\sum_j e^{z_j}}</math> || (0, 1) || Used in output layer for multi-class classification
    |}
    函数 公式 范围 备注
    Sigmoid $ \sigma(z) = \frac{1}{1+e^{-z}} $ (0, 1) 历史上很流行；存在梯度消失问题
    Tanh $ \tanh(z) = \frac{e^z - e^{-z}}{e^z + e^{-z}} $ (−1, 1) 以零为中心；对大输入仍会饱和
    ReLU $ \max(0, z) $ [0, ∞) 现代网络中的默认选择；可能导致"死神经元"
    Leaky ReLU $ \max(\alpha z, z) $，其中 $ \alpha > 0 $ 较小 (−∞, ∞) 解决死神经元问题
    Softmax $ \frac{e^{z_i}}{\sum_j e^{z_j}} $ (0, 1) 用于多分类输出层
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