Language
Country/Area
No result found
Selection of activation function: Why do modern models such as BERT and ResNet rely so much on GELU and ReLU?
In the architecture of artificial neural networks, the choice of activation function plays a crucial role. These functions calculate the output of each node, depending on its individual inputs and their weights, regulating the transfer of information. As deep learning technology continues to advance, activation functions have undergone multiple evolutions, with GELU and ReLU becoming the most popular choices today. This article will explore the mathematical properties behind these activation functions and their application in contemporary models.
Types and characteristics of activation functions
Activation functions can basically be divided into three categories: ridge function, radial function and fold function. When we take into account their different properties, such as nonlinearity, range, and whether they are continuously differentiable, we can understand why certain activation functions perform better in certain architectures.
"In the deep learning literature, the nonlinear nature of the activation function allows a two-layer neural network to be proven to be a universal function approximator."
According to the "Universal Approximation Theorem", a neural network with a nonlinear activation function can approximate any continuous function. This is the importance of the activation function. The nonlinear characteristics of GELU and ReLU provide stronger expression capabilities, allowing modern models, including BERT and ResNet, to handle complex problems.
Advantages of GELU and ReLU
GELU (Gaussian Error Linear Unit) is widely used in the BERT model. The function is designed with full consideration of the continuity of the gradient, which is crucial for the flow of information. Compared with the traditional ReLU (Rectified Linear Unit), GELU can adjust the activation output within a wider range, which is helpful for stability and convergence speed.
"The output of GELU adopts the characteristics of Gaussian error, making it better than ReLU in some cases, especially in the training of complex models."
ReLU, on the other hand, is favored for its simplicity and computational efficiency. Due to its sparse activation characteristics, ReLU can help neural networks reduce the computational burden in feature learning and promote faster training. Since the output of ReLU is zero below zero, this property makes it less susceptible to the vanishing gradient problem, so it is widely used in models such as AlexNet and ResNet.
The impact of nonlinear activation functions
The nonlinear characteristics of the activation function are one of the key factors for its success. Nonlinearity allows neural networks to capture and learn complex patterns in input data. In the actual training process, if a linear activation function is selected, nonlinear problems will not be effectively learned. Therefore, when we use nonlinear activation functions, especially in multi-layer neural networks, we are able to take full advantage of their capabilities.
“Choosing an appropriate activation function can have a profound impact on the overall performance of the model.”
Limitations and challenges of GELU and ReLU
While both GELU and ReLU bring numerous advantages, they also face challenges in specific situations. The complexity of GELU means that it may face efficiency bottlenecks in certain computing platforms or implementations. ReLU has the "dead ReLU" problem, which means that during training, some nodes will remain zero for a long time, resulting in the inability to update their weights. Therefore, when designing a model, one needs to carefully consider the choice of activation function and choose the function that is most suitable for the specific task.
The future of activation functions
With the rise of quantum computing and new neural network architectures, we may see further evolution of activation functions. Quantum neural networks have begun to explore how to achieve more efficient nonlinear activation without measuring the output of each perceptron. Perhaps more innovative activation function designs will appear in the future.
In the continued development of deep learning, the choice of activation function is still crucial to the performance of the model. Faced with changing needs and challenges, can researchers and engineers find new activation functions or improve existing methods to meet future needs?
