Activation functions are an essential component of artificial neural networks, playing a crucial role in determining the output of the network. They introduce non-linear properties to the network, allowing it to learn complex patterns from data.

Common Activation Functions

Here are some of the most commonly used activation functions:

  • Sigmoid: Maps any input to a value between 0 and 1.
    • Sigmoid_Activation_Function
  • ReLU (Rectified Linear Unit): Outputs the input directly if it is positive, otherwise, it outputs zero.
    • ReLU_Activation_Function
  • Tanh (Hyperbolic Tangent): Maps any input to a value between -1 and 1.
    • Tanh_Activation_Function
  • Softmax: Used in multi-class classification, it outputs a probability distribution over the classes.

Choosing the Right Activation Function

The choice of activation function depends on the specific problem and the architecture of the neural network. For example, ReLU is often used in hidden layers because it helps prevent the vanishing gradient problem.

For further reading on neural networks and activation functions, you can visit our Neural Networks Tutorial.

Activation函数是人工神经网络的核心组成部分，它们在确定网络输出方面发挥着至关重要的作用。它们为网络引入了非线性属性，使网络能够从数据中学习复杂的模式。

常用激活函数

以下是一些最常用的激活函数：

  • Sigmoid函数：将任何输入映射到0和1之间的值。
    • Sigmoid_Activation_Function
  • ReLU（修正线性单元）：如果输入为正，则直接输出输入值，否则输出零。
    • ReLU_Activation_Function
  • Tanh（双曲正切）：将任何输入映射到-1和1之间的值。
    • Tanh_Activation_Function
  • Softmax：在多类分类中使用，它输出一个关于类别的概率分布。

选择合适的激活函数

选择激活函数取决于具体问题和神经网络的架构。例如，ReLU通常用于隐藏层，因为它有助于防止梯度消失问题。

想要了解更多关于神经网络和激活函数的信息，您可以访问我们的神经网络教程。