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The charm of activation functions: Why will tanh and sigmoid change the fate of neural networks?
In the world of artificial intelligence, neural network technology is advancing rapidly. Among them, the activation function plays a crucial role. What makes these activation functions, such as tanh and sigmoid, the cornerstone of artificial neural networks? This article will deeply explore the historical background and operating principles of these functions, and analyze how they change the fate of neural networks.
Basics of activation functions
In neural networks, the main task of the activation function is to introduce nonlinearity, so that even when multiple linear transformations are spliced, the network can still capture more complex feature information.
The two activation functions, tanh and sigmoid, are used in different scenarios and have become the first choice for widespread application of neural networks.
The output range of the tanh function is from -1 to 1, making it very suitable for data with positive and negative characteristics, while the output range of the sigmoid function is from 0 to 1, which is very suitable for practical applications that require probability output.
The learning process of neural network
The learning process of neural networks is carried out by adjusting the connection weights between neurons. Based on the difference between the processing result of each input data and the expected result, the neural network uses a method called backpropagation to learn.
This supervised learning method enables the neural network to continuously adjust to achieve expected results, becoming the core of deep learning.
Specifically, each activation function has important data conversion capabilities at each layer of the network, affecting the final output. Without an appropriate activation function, the model will only be able to perform linear transformations, and will not be able to solve complex nonlinear problems.
Historical differences between tanh and sigmoid
In the neural network research of the last century, tanh and sigmoid were one of the earliest activation functions used. Because they can effectively alleviate the vanishing gradient problem, early deep learning models can work effectively in deeper networks.
The performance of these functions had a profound impact on the development of neural networks, and even promoted the emergence of more complex activation functions later.
For example, ReLU (linear rectification unit) was proposed after understanding the shortcomings of the sigmoid function at extreme values. This process shows the evolution of the activation function and its important impact on learning efficiency and accuracy.
Future challenges and prospects
With the continuous improvement of computing power and the growth of data sets, the selection of activation functions has become a key factor in model performance. Although tanh and sigmoid have laid the foundation to a certain extent, they may face stronger challenges in the future.
With the emergence of new technologies, new activation functions such as Swish and Mish are gradually receiving attention. These new activation functions not only overcome the shortcomings of the old functions, but also help build more efficient neural networks.
Conclusion: The significance of activation function
In short, tanh and sigmoid are important components of artificial neural networks, and their emergence and development have a profound impact on the entire field. With the advancement of technology, more novel activation functions will be born in the future, further pushing the boundaries of artificial intelligence. In the face of this rapidly developing field, let us think: In the coming AI era, can these activation functions once again change the fate of the entire technology?
