Influence of Activation Functions in Deep Learning Models
Keywords:
Activation functions, Deep learning, GELU, Leaky ReLU, ReLU, Sigmoid, Swish, TanhAbstract
Deep learning has become one of the most significant areas of artificial intelligence due to its ability to solve complex problems across various fields, including computer vision, natural language processing, healthcare, finance, and cybersecurity. A key component of deep learning models is the activation function, which introduces non-linearity into artificial neural networks and enables them to learn complex patterns from data. The choice of an activation function greatly influences a model’s learning capability, convergence speed, computational efficiency, and prediction accuracy. This study presents a comparative analysis of commonly used activation functions in deep learning models, including Sigmoid, Hyperbolic Tangent (Tanh), Rectified Linear Unit (ReLU), Leaky ReLU, Exponential Linear Unit (ELU), Swish, Gaussian Error Linear Unit (GELU), and Softmax. The study reviews their mathematical properties, advantages, limitations, and practical applications, as well as findings from previous research. In addition, the selected activation functions are compared based on performance metrics such as accuracy, convergence rate, training efficiency, and computational complexity. The findings indicate that while traditional activation functions such as Sigmoid and Tanh remain useful for specific applications, they are limited by issues such as the vanishing gradient problem. Modern activation functions, particularly ReLU and its variants, demonstrate improved performance in deep neural networks due to faster convergence and better computational efficiency. Furthermore, Swish and GELU have shown superior performance in several advanced deep learning applications. The study concludes that no single activation function is universally optimal, and the choice of an activation function should depend on the specific problem, model architecture, and application requirements.
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