Keras, an open-source neural network library, is widely praised for its ease of use and flexibility. When dealing with large datasets that don't fit entirely into memory, Keras offers a robust solution through its `fit_generator()` method. This method allows you to train your model using Python generators or the `keras.utils.Sequence` class.

While `fit_generator()` is a powerful tool, understanding its behavior, especially when called multiple times, is vital for efficient and accurate model training. This article delves into the technicalities and nuances of using `fit_generator()` repeatedly and addresses common scenarios and potential pitfalls.

Overview of `fit_generator()`

The `fit_generator()` function is used to train a deep learning model in Keras using data that is generated batch-by-batch by a generator. The generator yields batches of input samples and their corresponding target outputs indefinitely which is particularly useful for large datasets that do not fit into memory.

Key Arguments:

• generator: A generator that yields tuples of `(inputs, targets)` or `(inputs, targets, sample_weights)`.
• steps_per_epoch: Total number of steps (batches of samples) to iterate before declaring one epoch finished and starting the next epoch.
• epochs: Total number of iterations over the entire dataset.
• callbacks: List of callback instances to apply during training.
• validation_data: This can be a generator for validation or a tuple of arrays.

Calling `fit_generator()` Multiple Times

Calling `fit_generator()` multiple times for a single model can be useful in several scenarios, such as incremental training, different data augmentations, or staged training for complex models. However, it is crucial to understand how the model state is preserved and how this affects the convergence and performance.

Scenarios for Multiple Calls

1. Resume Training: You might need to stop training due to time constraints and resume it later. By calling `fit_generator()` again, you can continue training the model where you left off, leveraging Keras's ability to save and load model weights.
2. Staged Training: Certain architectures, especially GANs or complex layered models, might require sequential training of different parts. You can train one part of the model, adjust it based on the results, and then continue training the entire model or other sections using subsequent calls to `fit_generator()`.
3. Dynamic Data Augmentation: If you're experimenting with different data augmentation techniques, you can call `fit_generator()` each time with a different generator that applies distinct transformations.
4. Batch Size Adjustments: You might start training with a smaller batch size for the initial epochs and increase it as the model converges. Different calls to `fit_generator()` can help manage this process.

Technical Considerations

When using `fit_generator()` multiple times, consider these factors:

• Model State: The model retains its state between calls, which means learned weights are preserved unless explicitly reset.
• Optimizer State: The optimizer’s state (e.g., momenta, rmsprop caches) is maintained. If you wish to reset it, you need to recompile the model by calling `model.compile()`.
• Callbacks State: If using callbacks like LearningRateScheduler or EarlyStopping, ensure they are appropriately configured for subsequent calls, as their states do not persist between calls unless explicitly saved and restored.
• Data Shuffling: Whether or not data is shuffled depends on the generator design. Pay attention to shuffling to prevent overfitting or learning due to fixed data order.

Example

Here's an example illustrating how `fit_generator()` can be used multiple times to perform staged training: