What is the loss function in PyTorch?
In PyTorch, a loss function is used to measure the difference between the model’s predicted output and the actual labels. The goal of the loss function during training a neural network is to minimize the model’s prediction error, allowing the model to better fit the training data and perform well on unseen data.
PyTorch offers a variety of loss functions, some commonly used ones include CrossEntropyLoss, MSELoss, and BCELoss. Users can choose the appropriate loss function based on their task requirements to train their model.