文件名称：visualize the neural network.pdf
文件大小：4.35MB
文件格式：PDF
更新时间：2022-11-10 06:16:32
人工智能 alphaGo
Neural network training relies on our ability to find “good” minimizers of highly non-convex loss functions. It is well known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and well- chosen training parameters (batch size, learning rate, optimizer) produce minimiz- ers that generalize better. However, the reasons for these differences, and their effect on the underlying loss landscape, is not well understood. In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple “filter normalization” method that helps us visualize loss function curvature, and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network archi- tecture affects the loss landscape, and how training parameters affect the shape of minimizers.