Chapter 6: Model Training Workflow with PyTorch
Abstract : The PyTorch model training workflow typically follows a series of fundamental steps to prepare data, define a model, train it, evaluate its performance, and finally, save and load it for future use. 1. Getting Data Ready: This initial stage involves preparing your dataset for training. This includes: Data Loading: Using torch.utils.data.Dataset to represent your data and torch.utils.data.DataLoader to efficiently load and batch it. Preprocessing: Cleaning, transforming, and augmenting your data as needed (e.g., normalization, resizing images). 2. Defining and Building a Model: This step involves creating the neural network architecture that will learn patterns from your data. Model Definition: Subclassing torch.nn.Module to define the layers and forward pass of your model. Loss Function: Choosing an appropriate loss function (e.g., nn.MSELoss for regression, nn.CrossEntropyLoss ...