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Having access to internal nodes also means you can access an intermediate layer output and leverage it in a new model. This is a killer feature for feature extraction, fine-tuning, and ensembling. Let's add an extra output to the model above:pic.twitter.com/gCxafm21UF
3. The model is a data structure, not a piece of bytecode. This means it can be cleanly serialized and deserialized -- even across platforms. keras.Model.from_config(functional_model.get_config()) reconstructs the exact same model as the original.
If your model is a Python subclass, to serialize it you could either: a. Pickle the bytecode -- which it completely unsafe, won't work for production, and won't work across platforms
b. Save it as a SavedModel -- which is a form of one-way export (of the TF graph) and won't let you reconstruct the exact same Python object. A graph of layers is a data structure; defining and saving it as a data structure is the intuitive thing to do.
Many runtimes other than Python TensorFlow understand the Keras graph-of-layers format, such as TF.js, CoreML, DeepLearning4J... A high level, human-readable saving format is much easier to implement for third-party platforms.
A last advantage of the Functional API I haven't listed here is that it is much less verbose, because it is less redundant (no need to list/name each layer twice). Consider this subclassed VAE vs. an equivalent Functional model...pic.twitter.com/hkxVE8eXlZ
Note that you don't have to inline your Functional model definitions all the time -- complex models should be broken down into stateless functions (one function per architectural block). Here's an example of a Transformer for timeseries classification.pic.twitter.com/gBi4mO2FyT
That's it for this tweetorial. Feel free to chime in with your own takes on pros and cons of the Functional and subclassing approaches!
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