Optimization updates model parameters to reduce loss during training. In PyTorch, an optimizer uses gradients from the loss to decide how much each parameter should change. Adam extends traditional gradient descent by considering gradients from previous iterations, not only the current one. section 2.3.2 section 10.3
A typical training step connects the optimizer to the model and loss:
- Choose a learning rate, such as 0.001; tune it empirically or with a validation set. section 2.3.2
- Compute predictions and a loss, then call loss.backward() to calculate parameter gradients. section 2.3.2
- Clear old gradients and call optimizer.step() to apply the update. section 2.3.2
Binary image classification
An Adam optimizer trains a binary classifier that distinguishes ankle boots from t-shirts. The model uses BCELoss, processes batches for multiple epochs, and updates parameters after each backward pass. section 2.3.2
lr=0.001 optimizer=torch.optim.Adam(binary_model.parameters(),lr=lr) loss_fn=nn.BCELoss()
This compact listing shows how PyTorch binds a learning rate, model parameters, Adam, and binary cross-entropy loss. section 2.3.2
