Activation Functions and Their Properties

Deep learning is a subset of machine learning that focuses on training artificial neural networks to learn and make predictions. Activation functions play a crucial role in deep learning networks by introducing non-linearity to the network's outputs, enabling it to learn complex patterns and make accurate predictions. In this article, we will explore different activation functions commonly used in deep learning and discuss their properties.

Sigmoid Activation Function

The sigmoid activation function is one of the earliest activation functions used in neural networks. Also known as the logistic function, it takes any real-valued number as input and maps it to a value between 0 and 1. The formula for the sigmoid activation function is:

sigmoid(x) = 1 / (1 + e^(-x))

Properties:

• Sigmoid activation functions are smooth and continuously differentiable, making them easy to work with in gradient-based optimization algorithms.
• They squash the input into a range between 0 and 1, rendering them useful in binary classification tasks.
• Sigmoid functions suffer from the vanishing gradient problem, where gradients become very small for extreme inputs, leading to slow convergence during training.

ReLU Activation Function

Rectified Linear Unit (ReLU) is currently the most widely used activation function in deep learning. It is defined as the positive part of the input, i.e., it returns the input if it is positive, and zero otherwise. The formula for the ReLU activation function is:

ReLU(x) = max(0, x)

Properties:

• ReLU activation functions are computationally efficient to compute compared to sigmoid and tanh functions.
• They are less prone to the vanishing gradient problem, allowing deep networks to converge faster during training.
• However, ReLU functions suffer from the dying ReLU problem, where a large number of neurons can irreversibly die during training, causing the network to underperform.

Tanh Activation Function

The hyperbolic tangent (tanh) activation function is similar to the sigmoid activation function but maps the input to a value between -1 and 1. The formula for the tanh activation function is:

tanh(x) = (e^x - e^(-x)) / (e^x + e^(-x))

Properties:

• Tanh activation functions are zero-centered, which makes the computations easier compared to sigmoid functions.
• They are generally preferred over sigmoid functions in hidden layers of neural networks due to their stronger gradients.
• Like sigmoid functions, tanh functions can still suffer from the vanishing gradient problem.

Leaky ReLU Activation Function

Leaky ReLU is an extension of the ReLU activation function that aims to address the dying ReLU problem. It introduces a small constant slope for negative inputs, preventing neurons from dying completely. The formula for the leaky ReLU activation function is:

LeakyReLU(x) = max(0.01x, x)

Properties:

• Leaky ReLU activation functions prevent the dying ReLU problem and allow the network to learn from negative input values.
• They are computationally efficient and do not suffer from the vanishing gradient problem.
• However, setting the right slope for the negative inputs is considered a hyperparameter and should be carefully chosen.

Conclusion

Activation functions are vital components of deep learning networks that introduce non-linearity and enable neural networks to model complex relationships in data. In this article, we discussed some commonly used activation functions, including sigmoid, ReLU, tanh, and leaky ReLU functions. Each activation function has its own properties and characteristics, and choosing the right function depends on the specific requirements of the deep learning task.