How Do You Visualize Your Neural Network Model to Understand the Flow of Information?

General
1
  • When working with neural networks, what tools or techniques do you use to visualize the architecture and better understand the flow of information through the layers? How do you ensure that you grasp the internal workings of your model?
  1. Visualization Tools:
  • What tools or libraries do you use to visualize your model’s architecture (e.g., TensorBoard, Netron, Keras Plot Model, PyTorch’s torchviz, NN-SVG)?
  • How do these tools help you see the flow of data and the connections between layers?
  1. Activation and Gradient Visualizations:
  • How do you visualize activations or gradients as data passes through the network? Do you use methods like Grad-CAM, saliency maps, or activation maximization to better understand what your model is learning?
  1. Layer and Weight Visualizations:
  • Have you used techniques to visualize weights or filters in layers like CNNs? How do you use visualizations of kernels or feature maps to better interpret the features being extracted?
  1. Dimensionality Reduction:
  • How do you use techniques like t-SNE or PCA to visualize high-dimensional data representations from deeper layers of your network?
  1. TensorBoard/Other Tools for Monitoring:
  • If you’re using tools like TensorBoard, how do you monitor training performance and visualize metrics like loss, accuracy, or gradient flow? What insights do you gain from these visualizations?