Deep Learning with PyTorch Step-by-Step: Part I - Fundamentals/

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Model Training and Predictions

Model Training and Predictions

Learn about running a classy pipeline for the model training and making predictions steps.

We'll cover the following...

Starting steps

We start by instantiating the StepByStep class with the corresponding arguments. Next, we set its loaders using the appropriately named function set_loaders. Then, we set up an interface with TensorBoard, and then name our experiment “classy.”

sbs = StepByStep(model, loss_fn, optimizer)
sbs.set_loaders(train_loader, val_loader)
sbs.set_tensorboard('classy')

One important thing to notice is that the model attribute of the sbs object is the same object as the model variable created in the model configuration. It is not a ...