The activation function is one of the most vital components in the CNN model. They're utilized to learn and approximate any form of network variable-to-variable association that's both continuous and complex. In simple terms, it determines which model information should flow in the forward direction and which should not at the network's end. It gives the network non-linearity. The ReLU, Softmax, tanH, and Sigmoid functions are some of the most often utilized activation functions. All of these functions have distinct uses. For a 2-class CNN model, sigmoid and softmax functions are favored, whereas softmax is typically employed for multi-class classification.