Keras, a high-level neural networks API, is built on top of lower-level libraries, like TensorFlow, and provides an accessible interface for creating and training deep learning models. A fundamental part of understanding Keras involves distinguishing among three often-confused methods within the `Model` class: `call`, `call`, and `predict`. Though they may seem similar, their purposes and use cases are distinct.

1. Understanding the Methods

`call` Method

In Keras, the `call` method is a special built-in method in Python used to make a class instance callable. This method internally invokes the `call` method when you pass inputs to an instance of the Keras `Model` class, i.e., `model(inputs)`.

• Usage: You would rarely override `call` in custom layers or models. Instead, you generally define your custom logic in the `call` method.
• Technical Overview: When a model instance is called, it processes the inputs through the model's graph defined by the `call` method.

`call` Method

The `call` method is where you define the forward computation logic of a model in Keras. This method is executed whenever the model processes an input data batch.

• Purpose: Any custom computations, such as defining how layers are connected or complex input manipulations, are implemented in this method.
• Flexibility: Overriding this function allows for specifying the model's behavior. Typical operations include using TensorFlow operations and other neural network layers.
• Example:
• Functionality: It handles typical preprocessing and outputs consistent with the training process. This method elaborates on using batches and distributing inputs over multiple devices if applicable.
• Considerations: `predict` runs in "inference mode", which means that certain layer types may behave differently (e.g., dropout or batch normalization layers).
• Example:
• Implementation: `call` provides the backbone of forward computation, while `call` acts as the interface. On the other hand, `predict` simplifies the process of obtaining model outputs.
• Intended Use:
  • `call` is intended for defining custom model behavior.
  • `call` automatically wraps around `call`.
  • `predict` is tailored for making predictions post-training.
• Batch Normalization: Uses batch statistics during training and moving average during inference.
• Dropout: Applies random dropout during training, but remains inactive during inference.