I've started learning TensorFlow and I'm confused about when to use the Sequential model versus the Functional API. Most tutorials use Sequential for simple stacks, but I've seen some advanced projects using the Functional approach. What are the limitations of the Sequential API, and is there a performance difference between the two when training on large-scale datasets?
3 answers
There is no performance difference in terms of training speed; it's all about architectural flexibility. The Sequential API is great for a single stack of layers where each layer has exactly one input and one output. However, it cannot handle models with multiple inputs (like image + metadata), multiple outputs, or shared layers (like Siamese networks). The Functional API treats layers as functions and allows you to create directed acyclic graphs (DAGs). If you plan on building ResNets with skip connections, you must use the Functional API.
Do you have a specific project in mind that requires non-linear topology, or are you just trying to future-proof your learning path for more complex research?
Do you have a specific project in mind that requires non-linear topology, or are you just trying to future-proof your learning path for more complex research?
Eric, I think it's always better to learn the Functional API early. While Sequential is easy, the Functional API forces you to understand how tensors flow between layers. For instance, if Sarah ever wants to do Transfer Learning and branch off an intermediate layer of a pre-trained VGG16 model, the Sequential API simply won't cut it. It’s a vital skill for anyone moving past the "hello world" stage of machine learning.