Zufallsinitialisierung

Zufallsinitialisierung refers to the process of assigning random values to the parameters of a neural network at the beginning of its training. This is a crucial step because if all weights were initialized to the same value, the network would not be able to learn effectively. During backpropagation, all neurons in a layer would receive the same gradient and update their weights identically, leading to a situation where all neurons in a layer would perform the same function.

The choice of initialization strategy can significantly impact the training process and the final performance of the neural network. Poor initialization can lead to problems such as vanishing or exploding gradients, which can stall or destabilize training. Common initialization techniques include Xavier initialization (also known as Glorot initialization) and He initialization. These methods aim to keep the variance of the activations and gradients within a reasonable range across layers, promoting more stable and efficient learning.

The goal of Zufallsinitialisierung is to break symmetry and allow different neurons to learn different features. The random values are typically drawn from specific probability distributions, such as uniform or normal distributions, with parameters chosen based on the architecture of the network and the activation functions used. Experimentation with different initialization methods is often necessary to find the optimal approach for a given task and model.