How to Evaluate the Performance of PyTorch Models
Designing a deep learning model is sometimes an art. There are a lot of decision points, and it is not easy to tell what is the best. One way to come up with a design is by trial and error and evaluating the result on real data. Therefore, it is important to have a scientific method to evaluate the performance of your neural network and deep learning models. In fact, it is also the same method to compare any kind of machine learning models on a particular usage.
In this post, you will discover the received workflow to robustly evaluate model performance. In the examples, we will use PyTorch to build our models, but the method can also be