Model Evaluation: evaluate() Method

When training deep neural networks, model architecture is defined, and methods like fit() are employed. Monitoring performance on validation data provides valuable information during this learning process. However, the validation set is often used implicitly or explicitly to make decisions during training, such as adjusting hyperparameters or applying early stopping. Therefore, its performance metric might not be a completely unbiased estimate of how well your model will perform on entirely new, unseen data.

To get a final, objective assessment of your model's generalization ability, you need to evaluate it on a separate dataset that was held out and never used during training or validation tuning. This is the role of the test set, and Keras provides the evaluate() method for this specific purpose.

Assessing Generalization with evaluate()

The evaluate() method computes the loss and any other metrics you specified during the compile() step on a dataset provided as input. It operates in a single pass over the data, making predictions and comparing them to the true labels to calculate the performance metrics. Unlike fit(), it does not perform any weight updates. Its sole purpose is assessment.

Think of it like this:

• Training data: The material the student studies.
• Validation data: Practice quizzes taken during studying to check understanding and adjust study habits.
• Test data: The final exam, taken after all studying is complete, to measure overall mastery on questions not seen before.

Using evaluate() on the test set provides this "final exam" score for your model.

How to Use evaluate()

Using the method is straightforward. Assuming you have a trained model and your test data split into features (x_test) and labels (y_test), you call evaluate() like this:

# Assume 'model' is your trained Keras model
# Assume 'x_test' and 'y_test' are your test features and labels

# Evaluate the model on the test data
results = model.evaluate(x_test, y_test, batch_size=128) # You can optionally specify a batch size

# Print the results
print("Test Loss:", results[0])
print("Test Accuracy:", results[1]) # Assuming accuracy was the second metric compiled

# If you have more metrics, access them by index:
# print("Test Metric 2:", results[2])
# ...and so on.

The evaluate() method requires the test features and the corresponding true labels. It returns a list (or a scalar if only loss was compiled) containing the loss value followed by the values of any metrics specified in model.compile(). The order of the metrics in the returned list matches the order provided in the metrics argument during compilation.

For example, if you compiled your model like this:

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy', tf.keras.metrics.Precision()])

Then the list returned by model.evaluate(x_test, y_test) would contain:

1. results[0]: The categorical crossentropy loss calculated on the test set.
2. results[1]: The accuracy calculated on the test set.
3. results[2]: The precision calculated on the test set.

Interpreting Evaluation Results

The values returned by evaluate() represent your model's performance on data it has never encountered before. This is often considered the most reliable measure of how the model is likely to perform in a real application scenario.

It's informative to compare the test metrics (from evaluate()) with the final validation metrics observed during training (from the output of fit()).

• Similar Performance: If the test performance is close to the validation performance, it suggests your validation set was representative and your model generalizes reasonably well.
• Worse Test Performance: A significant drop in performance on the test set compared to the validation set might indicate that you implicitly overfitted to the validation set during model development or hyperparameter tuning. It could also mean the test set has slightly different characteristics than the training/validation sets.
• Better Test Performance: While less common, slightly better performance on the test set can happen due to statistical chance, especially with smaller test sets. A large improvement might warrant investigation into the data splits.

The following chart illustrates a typical scenario where test performance is slightly lower than validation performance, which itself is lower than training performance.

Comparison of final accuracy scores achieved on the training, validation, and test datasets. A slight drop from validation to test accuracy is common and expected.

In summary, model.evaluate() is the standard Keras function for obtaining a final performance assessment of your trained model on unseen data. It provides the critical metrics needed to understand how well your model generalizes, serving as a benchmark before deploying the model or deciding on next steps for improvement.