Flowchart of feature fusion and mapping in fully connected layers

This flowchart provides a systematic visualization framework for forward and backward propagation in fully connected layers of a neural network. The core computational logic is presented in a three-layer variable structure: x1-x3 represent the input feature vectors from convolutional or pooling layers, carrying the local semantic information extracted upstream; y1-y4 represent the weighted summation, bias stacking, and activation function processing of the input features by the hidden layer neurons, reflecting the global correlation and nonlinear transformation between features; z1-z4 are the output layer results, mapped to class scores or regression values, completing the final transformation from the feature space to the decision space. This flowchart not only clearly depicts the relationship between information flow and dimensional transformation but also provides structured guidance for model parameter initialization, gradient backpropagation, and the deployment of regularization strategies, serving as a fundamental tool for deep learning model design and optimization.