In the structure of a neural network, are the early or later layers more affected by vanishing gradients?

Question Analysis

The question is asking about the concept of vanishing gradients within the context of neural networks. Specifically, it seeks to understand which layers of a neural network are more susceptible to the issue of vanishing gradients. This is a technical question focused on the behavior of neural networks during training, particularly when using backpropagation to adjust weights.

Answer

In the structure of a neural network, the early layers (closer to the input layer) are more affected by vanishing gradients. This phenomenon occurs during the backpropagation process when gradients of the loss function are propagated backward through the network to update the weights. If the gradients are too small, they can diminish to nearly zero as they are passed back through each layer, particularly through activation functions like sigmoid or tanh. This results in very little or no change to the weights of the early layers, making it difficult for the network to learn effectively.

To mitigate this issue, techniques such as using ReLU activation functions, initializing weights properly (e.g., using Xavier or He initialization), or employing architectures like LSTMs or residual networks (ResNets) are commonly used.