Playing with PyTorch from Scratch: Data Visualization and Model Evaluation Techniques

This article introduces core skills of data visualization and model evaluation in PyTorch to facilitate efficient model debugging. For data visualization, Matplotlib can observe data distributions (e.g., histograms of MNIST samples and labels), and TensorBoard can monitor training processes (e.g., scalar changes, model structures). In model evaluation, classification tasks should focus on accuracy and confusion matrices (e.g., MNIST classification example), while regression tasks use MSE and MAE. In practice, using visualization to identify issues (e.g., confusion between "8" and "9") enables iterative model optimization. Advanced applications include GAN visualization and real-time metric calculation. Mastering these skills allows quick problem localization and data understanding, laying a foundation for developing complex models.