Keras: Why is loss different for train_on_batch() and test_on_batch()

When using Keras for training and testing neural networks, it is important to understand why the loss values calculated with train_on_batch() and test_on_batch() may differ even when the same input is being passed.

Training vs Testing

During training, the model is constantly updating its weights to minimize the loss function on the training data. This process involves making small adjustments to the model’s parameters based on the gradients of the loss function with respect to those parameters. As a result, the loss value calculated during training is specific to the data that the model has seen and the current state of the model’s weights.

On the other hand, testing (or evaluation) involves running the model on a separate set of data that it hasn’t seen before to evaluate its performance. This data is used to calculate the model’s generalization error, or how well it performs on unseen data. The loss value calculated during testing gives an indication of how well the model has learned to generalize from the training data to new data.

Why is loss different?

The difference in loss values between train_on_batch() and test_on_batch() can be attributed to several factors:

• Overfitting: The model may have overfit to the training data, meaning it has memorized the training examples instead of learning the underlying patterns. This can result in a low loss value during training but a higher loss value during testing when the model fails to generalize.
• Data augmentation: During training, data augmentation techniques such as random rotations, flips, and crops may be applied to the training data, which can make the model more robust. However, these augmentations are typically not used during testing, leading to differences in the loss values.
• Dropout and regularization: Techniques like dropout and weight regularization are commonly used during training to prevent overfitting. These techniques are usually turned off during testing, which can affect the model’s performance.

Conclusion

In summary, the difference in loss values between train_on_batch() and test_on_batch() is a natural consequence of the training and testing processes. It is important to monitor both training and testing loss values to ensure that the model is learning meaningful patterns from the data and is able to generalize well to new examples.