How does the activation function in a CNN affect the output?

The activation function in a Convolutional Neural Network (CNN) affects the output of the network by introducing non-linearity into the decision-making process. It determines the output of a neuron in the network based on the weighted sum of inputs it receives.

Types of Activation Functions:

• Sigmoid: The sigmoid function is a smooth, S-shaped function that ranges from 0 to 1. It is commonly used in binary classification problems, where the output is a probability of an object belonging to a certain class.
• ReLU (Rectified Linear Unit): The ReLU function is a simple function that sets the output to zero for negative values and the input value for positive values. It is often used in computer vision applications, where it can help to reduce the computational complexity of the network.
• Tanh: The tanh function is a hyperbolic tangent function that ranges from -1 to 1. It is similar to the sigmoid function, but it is centered around 0.
• Leaky ReLU: The leaky ReLU function is a variant of the ReLU function that has a small slope for negative values. This allows it to retain some of the information from negative inputs while still being computationally efficient.

How Activation Functions Work:

Activation functions take a weighted sum of inputs and apply a non-linear transformation to the result. This can be done using a variety of mathematical functions, such as sigmoid, ReLU, and tanh.

Effect on Output:

The activation function changes the shape of the decision boundary in the feature space. This can lead to the network learning more complex and accurate representations of the input data. For example, the sigmoid function can be used to learn a linear decision boundary, while the ReLU function can be used to learn a non-linear decision boundary.

Conclusion:

The activation function is an essential component of a CNN that introduces non-linearity into the decision-making process. This allows the network to learn more complex and accurate representations of the input data. The choice of activation function can have a significant impact on the performance of the network, so it is important to experiment with different functions to find the one that works best for a given task.