Description: Output activation is a crucial function applied in the final layer of a neural network, determining how the results generated by the network are interpreted. Its main goal is to transform the network’s output into a format that is useful for the specific task being performed, such as classification, regression, or prediction. Depending on the type of problem, different activation functions are used in the output layer. For example, in binary classification problems, the sigmoid function is often employed, which compresses the output between 0 and 1, facilitating interpretation as a probability. In the case of multi-class classification, the softmax function is commonly used, as it normalizes the outputs to sum to 1, allowing each value to represent the probability of belonging to each class. The choice of output activation function is fundamental, as it directly influences the model’s performance and the quality of predictions. Additionally, output activation can also affect the training process, as different functions may have different derivative properties, impacting the convergence of the optimization algorithm used during training.
