Home > Article > Technology peripherals > Analysis of commonly used AI activation functions: deep learning practice of Sigmoid, Tanh, ReLU and Softmax
Activation functions play a crucial role in deep learning. They can introduce nonlinear characteristics into neural networks, allowing the network to better learn and simulate complex input-output relationships. The correct selection and use of activation functions has an important impact on the performance and training effect of neural networks
This article will introduce four commonly used activation functions: Sigmoid, Tanh, ReLU and Softmax, from the introduction, usage scenarios, advantages, The shortcomings and optimization solutions are discussed in five dimensions to provide you with a comprehensive understanding of the activation function.
##1. Sigmoid functionSIgmoid function formula
Introduction: Sigmoid function is a A commonly used nonlinear function that can map any real number to between 0 and 1.It is often used to convert unnormalized predicted values into probability distributions.
SIgmoid function image
Application scenario:Optimization plan:
Tanh function formula
Introduction: Tanh function is Sigmoid Hyperbolic version of the function that maps any real number to between -1 and 1.
Tanh function image
Application scenario: When a function steeper than Sigmoid is required, or in some cases where the range of -1 to 1 is required output in specific applications. The following are the advantages: It provides a larger dynamic range and a steeper curve, which can speed up the convergence speedThe disadvantage of the Tanh function is that when the input is close to ±1, its derivative is rapid Close to 0, causing the problem of gradient disappearanceOptimization plan:
ReLU function formulaIntroduction: The ReLU activation function is a simple nonlinear function, and its mathematical expression is f(x) = max(0,
x). When the input value is greater than 0, the ReLU function outputs the value; when the input value is less than or equal to 0, the ReLU function outputs 0.
ReLU function image Application scenario: ReLU activation function is widely used in deep learning models, especially in convolutional neural networks (CNN) middle. Its main advantages are that it is simple to calculate, can effectively alleviate the vanishing gradient problem, and
can accelerate model training. Therefore, ReLU is often used as the preferred activation function when training deep neural networks.The following are the advantages: Disadvantages: Optimization plan: Softmax function formula Introduction : Softmax is a commonly used activation function, mainly used in multi-classification problems, which can convert input neurons into probability distributions. Its main feature is that the output value range is between 0-1, and the sum of all output values is 1. Softmax calculation process Application scenarios: The following are the advantages: In multi-classification problems, a relative probability value can be provided for each category to facilitate subsequent decision-making and classification. Disadvantages: There will be gradient disappearance or gradient explosion problems. Optimization scheme:
4. Softmax function
The above is the detailed content of Analysis of commonly used AI activation functions: deep learning practice of Sigmoid, Tanh, ReLU and Softmax. For more information, please follow other related articles on the PHP Chinese website!