What are the properties of an activation function?
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Activation functions in neural networks play a crucial role in shaping the outputs of artificial neurons and influencing the network's performance. These functions are essential for enabling non-linear transformations, aiding in faster convergence during training, and preventing issues like vanishing or exploding gradients. The choice of activation function impacts the network's initialisation, training time, and overall accuracy. Smooth activation functions like tanh and swish can lead to faster training when the data dimension is large, while non-smooth functions like ReLU are beneficial for highly overparametrized networks. Additionally, the linear region around the origin of an activation function is crucial for avoiding convergence problems and achieving better performance in practice. Understanding the properties and characteristics of activation functions is vital for optimizing neural network models across various tasks and datasets.
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Open access•Posted Content | Activation functions shape neural network outputs. This paper provides an overview of popular activation functions and their properties, aiding in understanding and applying neural networks effectively. |
Open access•Proceedings Article 01 Apr 2020 | Activation functions can be smooth or non-smooth. Smooth functions like tanh, swish, and polynomial require large data dimensions for fast training, while non-smooth functions like ReLU have large eigenvalues. |
Open access•Posted Content | Activation functions with a large linear region relative to bias variance help avoid rapid convergence of input correlations and gradient issues, improving deep network performance and training efficiency. |
| Activation functions in neural networks help convergence by introducing non-linearity. Experimentation in the paper suggests functions like SMod, Absolute/Mod, and scaled Swish improve performance in image classification tasks. | |
3 Citations | Activation functions determine output range and capabilities of neural networks. The paper introduces two new functions and compares them with 17 others, showing improved performance over commonly used functions like ReLU. |
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