Sequential model: Using the Sequential model, you can stack a series of layers in order to build a neural network model.
Using the Functional API allows for more flexibility in building neural network models. It enables the definition of multiple inputs, multiple outputs, and models with branching structures.
Subclassing model: By using Subclassing model, one can customize a neural network model by inheriting from the tf.keras.Model class, where the forward propagation process can be defined within the call method.
Estimator model: Using the Estimator model makes it easier to train, evaluate, and make predictions with models, especially for large datasets and distributed environments.
SavedModel is a way to build models by saving and loading them using the methods tf.saved_model.save() and tf.saved_model.load().
keras.Sequential: The Sequential model can be built using the tf.keras.Sequential() method.
keras.Model: By using the tf.keras.Model() method, it is possible to build a more flexible model that can define multiple inputs, multiple outputs, and branched structures.
Custom layer: By using custom layers, you can add specific layers to a model to meet certain requirements.
