Introduction to PyTorch Neural Networks: Fully Connected Layers and Backpropagation Principles

This paper introduces the basics of PyTorch neural networks, with a core focus on fully connected layers and backpropagation. A fully connected layer enables full connectivity between neurons of the previous layer and the current layer, producing an output calculated as the product of a weight matrix and the input, plus a bias vector. Forward propagation is the forward computation process of data from the input layer through fully connected layers and activation functions to the output layer, for example, in a two - layer network: input → fully connected → ReLU → fully connected → output. Backpropagation is the core of neural network learning, adjusting parameters through gradient descent. Based on the chain rule, it reversely calculates the gradient of the loss with respect to each parameter starting from the output layer. PyTorch's autograd automatically records the computation graph and completes gradient calculation. The process includes forward propagation, loss calculation, backpropagation (loss.backward()), and parameter update (using an optimizer like SGD). Key concepts: Fully connected layers implement feature combination, forward propagation performs forward computation, backpropagation minimizes loss through gradient descent, and automatic differentiation simplifies gradient calculation. Understanding these principles is conducive to model debugging and optimization.