Which layers in Keras are used for preprocessing data?

Preprocessing layers in Keras are specifically designed to transform and prepare input data for training models. These layers allow for data normalization, feature extraction, and other preprocessing steps to be seamlessly integrated into the model architecture. By using preprocessing layers, you can ensure that the data is appropriately formatted and scaled before it is passed to the subsequent layers in the model for training or inference.

Unique to preprocessing layers is their ability to be applied consistently during both training and evaluation phases, making them particularly useful when working with datasets that require transformations, such as resizing images or performing categorical encoding. This functionality of integrating preprocessing directly into the model pipeline helps streamline workflows and improves the model's robustness.

The other listed layer types, while integral to building neural networks, serve different purposes. Dense layers are used for building fully connected neural networks, convolutional layers are employed primarily in processing visual data, and dropout layers are utilized for regularization to prevent overfitting. None of these are suited for preprocessing the raw data input before it reaches the model's core architecture.