Integrating multiple inputs into a deep learning model is a common scenario, particularly in complex tasks where information from diverse sources needs to be leveraged. The `fit_generator` function in Keras provides an efficient way to train models with data that cannot fit entirely into memory, especially when dealing with varied input types. This article explores the use of `fit_generator` with multiple inputs.

Understanding `fit_generator`

The `fit_generator` function is designed for training models using data generators, allowing on-the-fly data processing. It's particularly useful for large datasets that don't fit into memory. This is particularly applicable for handling multiple inputs where you may have separate datasets of different modalities (e.g., images and text).

Key `Parameters`

• generator: A Python generator or Keras Sequence instance that yields either `(inputs, targets)` or `(inputs, targets, sample_weights)`.
• steps_per_epoch: The number of batch iterations before declaring an epoch complete.
• epochs: The number of epochs to train the model.
• callbacks: List of Keras callbacks.
• validation_data: Data on which to evaluate the loss and any model metrics at the end of each epoch.

Handling Multiple Inputs

When dealing with multiple inputs, the generator should yield the data as a tuple containing all input arrays, followed by the target output array. Each element in the tuple represents a different input or output. Let’s go through a detailed example.

Creating the Generator for Multiple Inputs

Suppose we have a model that takes both image and tabular data as inputs. Our generator should yield batches of image data, tabular data, and the corresponding labels.

• Data Preprocessing: Ensure each input variety (e.g., images and tabular data) undergoes appropriate preprocessing.
• Batch Size: Choose a batch size that balances computational efficiency with memory constraints.
• Callback Functions: Implement callbacks for dynamic adjustments, monitoring, or logging during training.