引导式测验 · 中等 等级

激活函数测验

检查网络为何需要非线性，以及 ReLU、sigmoid、tanh、softmax、Leaky ReLU 和 GELU 在隐藏层和输出层中的行为方式。

相关引导路径激活函数
问题 1 的 8

Why can stacking only linear layers not learn a curved decision boundary?

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  1. Why can stacking only linear layers not learn a curved decision boundary?
    • Each layer deletes all input features
    • The output becomes a probability distribution
    • Linear layers cannot be trained with backpropagation
    • Their composition is still a single linear transformation
  2. What is the output of ReLU for an input of −3?
    • −3
    • 0
    • 3
    • A class probability
  3. Why can sigmoid and tanh stall learning in deep networks when inputs reach their extremes?
    • Their gradients approach zero in saturated regions
    • They force every weight to be identical
    • They cannot represent bounded values
    • They always produce an undefined output
  4. A multiclass classifier needs output values that sum to one across classes. Which function does the guide use?
    • 泄漏ReLU
    • 谭
    • 软最大
    • ReLU
  5. What does the “dying ReLU” problem mean in the guide?
    • The training dataset loses labels
    • A unit stays on its zero-output side and receives no useful gradient there
    • The softmax outputs fail to sum to one
    • All positive inputs become negative
  6. Which variant is suggested when many ReLU units stay inactive?
    • 软最大
    • A purely linear hidden layer
    • Sigmoid at every hidden layer
    • Leaky ReLU, which allows a small negative-side response
  7. What feature of GELU does the guide associate with its use in transformer models?
    • It removes all nonlinear behavior
    • It always yields exact class probabilities
    • 平滑的响应，而不是 ReLU 的硬截断
    • 它解决了所有的训练不稳定问题
  8. 对于正输入，ReLU 的哪种梯度属性有助于保留反向传播的信号？
    • 它在正侧的斜率为 1
    • 它的斜率总是零
    • 它在任何地方都没有导数
    • 它将梯度归一化为类概率