forwardfunctie

A forward function is a core component in neural networks and other machine learning models. It represents the process of computing the output of a model given a set of inputs. This is achieved by passing the input data through a series of layers, each performing specific mathematical operations. Typically, these operations involve matrix multiplications and activation functions. The input data is transformed sequentially as it moves from one layer to the next, with the output of one layer serving as the input for the subsequent layer. The final layer produces the model's prediction or classification.

The forward function is essential for both training and inference. During training, it calculates the model's prediction, which is then compared to the actual target value to compute a loss. This loss is subsequently used to adjust the model's internal parameters through a process called backpropagation. During inference, the forward function is used solely to generate predictions on new, unseen data. The structure and complexity of the forward function depend entirely on the architecture of the neural network or machine learning model in question.