Chapter 16: Model Evaluation, Saving, and Loading with PyTorch
Abstract: Model Evaluation, Saving, and Loading with PyTorch 1. Model Evaluation: To evaluate a PyTorch model, follow these steps: Set the model to evaluation mode: Use model.eval() to disable dropout and batch normalization updates, ensuring consistent behavior during inference. Disable gradient calculations: Wrap your evaluation loop with torch.no_grad() to prevent unnecessary gradient computations, saving memory and speeding up the process. Iterate through the test or validation dataset: Feed input data to the model and obtain predictions. Calculate relevant metrics: Compare predictions with ground truth labels to compute metrics like accuracy, precision, recall, F1-score, or loss. Python import torch import torch . nn as nn # Assuming 'model', 'test_loader', and 'criterion' are defined model.eval() # Set model to evaluation mode total_loss = 0 correct_predictions = 0 total_samples = 0 with torch.no_grad(): # D...