Monitoring Training Progress

When a neural network undergoes training, its architecture is configured, parameters are initialized, and a loop executes forward passes, calculates loss, performs backpropagation, and updates weights through gradient descent. During this process, how can one determine if the network is genuinely learning anything useful? Simply letting the training loop run is not enough. Observing the process is necessary to understand its behavior and effectiveness. Monitoring training progress is fundamental to building successful models.

Without monitoring, you're essentially training blind. You won't know if the model is improving, how quickly it's learning, or when to stop the training process. Observing important metrics over time provides insights into the learning dynamics and helps diagnose potential problems.

Metrics to Track

During training, two primary types of metrics are indispensable:

1. Loss Function Value: This is the direct measure the optimization algorithm (like gradient descent) is trying to minimize. Tracking the loss tells you if the model's predictions are getting closer to the actual target values according to the chosen objective (e.g., Mean Squared Error for regression, Cross-Entropy Loss for classification).

   • Training Loss: Calculated on the data batch used for gradient computation and weight updates in the current iteration. A decreasing training loss generally indicates the model is learning to fit the training data.
   • Validation Loss: Calculated periodically (e.g., at the end of each epoch) on a separate validation dataset that the model does not train on. This metric estimates how well the model generalizes to unseen data. Comparing training loss and validation loss is important for identifying overfitting.

2. Performance Metrics: While loss guides the optimization, it might not be the most intuitive measure of how well the model performs a specific task. Therefore, we also track metrics relevant to the problem:

   • Accuracy: For classification tasks, this is often the percentage of correctly classified examples. Like loss, we track both training accuracy and validation accuracy.
   • Other Metrics: Depending on the task, other metrics might be more appropriate (e.g., Precision, Recall, F1-score for imbalanced classification; Mean Absolute Error (MAE) or R-squared for regression).

Observing Metrics Over Time

The real value comes from observing how these metrics change throughout the training process, typically across epochs.

• Logging: The simplest form of monitoring is printing the metric values to the console or a log file at regular intervals (e.g., after each epoch or even after a certain number of batches).

# Placement within a training loop
for epoch in range(num_epochs):
    # --- Training Phase ---
    model.train() # Set model to training mode
    running_train_loss = 0.0
    running_train_accuracy = 0.0
    for inputs, labels in train_loader:
        # Forward pass
        outputs = model(inputs)
        loss = criterion(outputs, labels)

        # Backward pass and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # Accumulate training metrics
        running_train_loss += loss.item() * inputs.size(0)
        # (Calculate accuracy based on outputs and labels)
        # running_train_accuracy += calculate_accuracy(outputs, labels) * inputs.size(0)

    epoch_train_loss = running_train_loss / len(train_loader.dataset)
    # epoch_train_accuracy = running_train_accuracy / len(train_loader.dataset)

    # --- Validation Phase ---
    model.eval() # Set model to evaluation mode
    running_val_loss = 0.0
    running_val_accuracy = 0.0
    with torch.no_grad(): # Disable gradient calculations
        for inputs, labels in validation_loader:
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            running_val_loss += loss.item() * inputs.size(0)
            # (Calculate accuracy based on outputs and labels)
            # running_val_accuracy += calculate_accuracy(outputs, labels) * inputs.size(0)

    epoch_val_loss = running_val_loss / len(validation_loader.dataset)
    # epoch_val_accuracy = running_val_accuracy / len(validation_loader.dataset)

    # Log metrics for the epoch
    print(f"Epoch {epoch+1}/{num_epochs} | "
          f"Train Loss: {epoch_train_loss:.4f} | "
          # f"Train Acc: {epoch_train_accuracy:.4f} | "
          f"Val Loss: {epoch_val_loss:.4f}")
          # f"Val Acc: {epoch_val_accuracy:.4f}")

    # Store metrics for later plotting
    train_loss_history.append(epoch_train_loss)
    val_loss_history.append(epoch_val_loss)
    # train_acc_history.append(epoch_train_accuracy)
    # val_acc_history.append(epoch_val_accuracy)

• Visualization: Plotting metrics against epochs provides a much clearer picture of the training trends. Visualizations make it easier to spot patterns, convergence behavior, and potential issues like overfitting or underfitting.

Interpreting Training Curves

Let's examine typical patterns you might see when plotting training and validation metrics:

• Healthy Training: Both training and validation loss decrease steadily and then plateau. Validation loss might be slightly higher than training loss, but they generally follow a similar trend. Correspondingly, training and validation accuracy (or other performance metrics) increase and plateau. This indicates the model is learning and generalizing well.

• Overfitting: Training loss continues to decrease, while validation loss starts to increase after some point (or flattens out significantly earlier than training loss). Similarly, training accuracy might keep rising while validation accuracy stagnates or even drops. This indicates the model is learning the training data too well, including its noise, and is losing its ability to generalize to new data. This is a common problem, and techniques discussed in Chapter 6 (like regularization and early stopping) are used to combat it.

• Underfitting: Both training and validation loss remain high or decrease very slowly and plateau at a high value. Performance metrics (like accuracy) stay low for both sets. This suggests the model might be too simple for the task, the training duration is insufficient, or there are issues with the optimization process (e.g., learning rate too small).

• Learning Rate Issues: The shape of the loss curve can sometimes hint at problems with the learning rate $\alpha$.

  • Too High: The loss might oscillate wildly or even diverge (increase uncontrollably).
  • Too Low: The loss decreases very slowly, potentially taking an excessively long time to converge or getting stuck in a suboptimal minimum.

Here's an example plot illustrating training and validation loss, potentially showing signs of overfitting later in training:

Training loss (blue) consistently decreases, while validation loss (orange) decreases initially but starts to rise after epoch 10, indicating overfitting.

Monitoring these metrics is not just about observing; it's about making informed decisions. Do you need to stop training early? Should you adjust the learning rate? Is a different model architecture needed? Does the model require regularization? Analyzing the training progress provides the necessary feedback to answer these questions and guide the development of effective neural networks.