Hi everyone,
I’m struggling to understand why each neuron becomes responsible for a different bad roasting area, given that they are all trained on the same data.
What stops multiple neurons from converging to the same weights and thus being responsible for the same area? Also, how do all neurons work together in sync if they aren’t, as I understand it, directly connected?
The hidden layer neuron weights are initialized to different random values, and this allows them to take different trajectories during training.
Overall the solution is found which gives the minimum cost.
Great questions!
First, neurons in the same layer receive identical input data but ultimately learn distinct functions for various reasons. As @TMosh mentioned, random initialization causes neurons to specialize in different parts of the input space. During training, if two neurons start learning similar functions, small differences in their weights and how gradients are propagated will push them to diverge and cover different aspects of the input space. This is related to a concept called “symmetry breaking.” Additionally, the loss function used during training often encourages the network to learn a wide range of features that can be used to minimize the overall loss more effectively.
Second, multiple factors stop neurons from converging to the same weights. As mentioned, even if two neurons initially start learning similar functions, slight differences in their gradients will cause their paths during optimization to diverge. In addition, each neuron receives gradient updates based on the entire network’s performance, taking into account not only the inputs but also the performance of other neurons in the network. The optimization process naturally encourages neurons to diversify rather than converge on the same solution.
Finally, neurons in the same layer are not directly connected, but they still work together in a complementary way. Each neuron in a layer is typically responsible for detecting different features from the input. For instance, in the coffee roasting lab, one neuron might focus on identifying when the temperature is too low, while another might focus on when the duration is too short. Then, neurons in subsequent layers build upon the features learned by the previous layer. Even if the neurons are not directly connected, the output from one layer becomes the input to the next.
