1. PyTorch Workflow Fundamentals
  1. Getting data ready
  1. Train model
  1. Saving and loading a model

Exercise

  1. Building a model
  1. Putting it all together
  1. Making predictions and evaluating a model (inference)

Split data into training and test sets

Data

PyTorch model building essentials

Checking the contents of a PyTorch model

Making predictions using torch.inference_mode()

torch.nn.Module

torch.optim

torch.nn.Parameter

def forward()

torch.nn

.parameters().

.state_dict().

optimization function

PyTorch training loop

loss function

PyTorch testing loop

(with torch.inference_mode(): y_preds = model_0(X_test)).

All predictions should be made with objects on the same device (e.g. data and model on GPU only or data and model on CPU only).

evaluation mode (model.eval()).

Saving a PyTorch model's state_dict()

Loading a saved PyTorch model's state_dict()

torch.save
torch.load
torch.nn.Module.load_state_dict

6.3 Training

6.2 Building a PyTorch linear model

6.4 Making predictions

6.1 Data

6.5 Saving and loading a model

Create Data

Create Know Parameters

Validation set (10-20%)

Testing set (10-20%)

Training set (60-80%)

Plot predictions

Draw

Create a Linear Regression model class + Forward

Zero gradients

Perform backpropagation on the loss

Calculate the loss

Update the optimizer (gradient descent)

Forward pass

Forward pass

Calculate the loss

Calulate evaluation metrics (optional)

Save the model state dict

Create model save path

Create models directory

Load the state_dict

Instantiate a new instance