Validation Loss

- **Strategy**: Use a learning rate scheduler to adjust the learning rate during training. 2. **Batch Size (`per_device_train_batch_size`)**: - **Description**: Number of samples processed before the model is updated. - **Range**:…

[Turn 6672] User: hmm, what kind of regularization techniques would you recommend for my model? [Turn 6673] Assistant: For your model, you can consider several regularization techniques to prevent overfitting and improve generalization. He…

3. **Early Stopping**: While not explicitly shown in the code above, you can implement early stopping by monitoring the validation loss and stopping training when it stops improving. This typically involves splitting your data into training…

loss = loss_fn(outputs, batch_labels) val_loss += loss.item() val_loss /= len(val_loader) print(f"Epoch [{epoch+1}/{num_epochs}], Val Loss: {val_loss:.4f}") # Early stopping if val_loss < best_v…

print(f"Early stopping triggered at epoch {epoch}") break print(f"Epoch {epoch+1}/{3000}, Training Loss: {loss.item():.4f}, Validation Loss: {avg_val_loss:.4f}") # Save the model torch.save(model.state_dict(), …

- Added an extra linear layer (`fc3`) to increase the depth of the model, allowing it to capture more complex patterns in the data. 4. **Weight Decay (L2 Regularization)**: - Included weight decay in the `optim.Adam` optimizer with a…

optimizer.step() # Zero gradients optimizer.zero_grad() # Validation loop scorer.eval() val_losses = [] with torch.no_grad(): for batch_inputs, batch_targets in val_loader: outpu…

- **Early Stopping**: Implement early stopping to halt training if the validation loss does not improve over a certain number of epochs. ### 9. **Model Complexity** - **Simplify the Model**: If the model is too complex, it might over…

1. **StepLR**: Decreases the learning rate by a factor of `gamma` every `step_size` epochs. 2. **ReduceLROnPlateau**: Reduces the learning rate when a metric has stopped improving. This is particularly useful for metrics like validation los…

- **Validation Loss**: In practice, you would typically compute the validation loss separately and pass it to the scheduler. This example uses the training loss for simplicity. - **Other Schedulers**: You can also experiment with other sche…

# Calculate average loss for the epoch avg_loss = running_loss / len(data_loader) print(f'Epoch [{epoch + 1}/100], Loss: {avg_loss:.4f}, LR: {optimizer.param_groups[0]["lr"]}') # Step the scheduler s…