François Chollet on Twitter: "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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François Chollet‏Verified account @fchollet Jul 12

1. Because the model has known input shapes, it's capable of running input validation checks, for easy debugging:pic.twitter.com/1B8E7GXmK1

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François Chollet‏Verified account @fchollet Jul 12

Further, it's even capable of standardizing inputs to what it expects: if you pass data of shape (batch_size,) to a model that expects (batch_size, 1), it will just reshape it. Likewise for dtype conversion (e.g. float64 will get converted to float32).

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François Chollet‏Verified account @fchollet Jul 12

2. You get access to the internal connectivity graph. This means you can plot the model, for instance. This is great for debugging. Like this:pic.twitter.com/ZnG6ym9yei

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François Chollet‏Verified account @fchollet Jul 12

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

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François Chollet‏Verified account @fchollet Jul 12

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.

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François Chollet‏Verified account @fchollet Jul 12

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

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François Chollet‏Verified account @fchollet Jul 12

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.

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François Chollet‏Verified account @fchollet Jul 12

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.

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François Chollet‏Verified account @fchollet Jul 12

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

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François Chollet‏Verified account @fchollet Jul 12

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

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François Chollet‏Verified account @fchollet Jul 12

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!

12:49 PM - 12 Jul 2021

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1. Alex Polozov‏ @Skiminok Jul 12
  Replying to @fchollet
  
  I just want to applaud the word "tweetorial", that's all
  
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1. Peter Mitrano‏ @PeterMitrano Jul 12
  Replying to @fchollet
  
  I often find myself switching to classes once I get too many parameters, too much state passing amongst functions in the model. Perhaps I should be doing that without subclassing model, just pass a dict or other data object around.
  
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1. Juan Pedro Fisanotti‏ @fisadev Jul 12
  Replying to @fchollet
  
  This thread tastes like lisp :) (I say that as a compliment!)
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1. Henrique Ribeiro‏ @henrique_dmr Jul 13
  Replying to @fchollet
  
  Great tutorial, you are truly a mvp. I'm looking forward for the next one.
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