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fchollet's profile
François Chollet
François Chollet
François Chollet
Verified account
@fchollet

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François CholletVerified account

@fchollet

Deep learning @google. Creator of Keras. Author of 'Deep Learning with Python'. Opinions are my own.

United States
fchollet.com
Joined August 2009

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    1. 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

      1 reply 0 retweets 38 likes
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    2. 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

      1 reply 0 retweets 27 likes
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    3. 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.

      1 reply 1 retweet 24 likes
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    4. 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

      2 replies 2 retweets 15 likes
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    5. 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.

      2 replies 3 retweets 20 likes
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    6. 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.

      1 reply 2 retweets 21 likes
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    7. 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

      1 reply 2 retweets 35 likes
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    8. 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

      2 replies 3 retweets 63 likes
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    9. 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!

      9 replies 3 retweets 40 likes
      Show this thread
    10. Santiago‏ @svpino Jul 12
      Replying to @fchollet

      The functional API is always the route I take. Even lately, I’ve found myself using it over the Sequential API out of habit. Subclassing only whenever I need to implement custom functionality in the training or testing loops.

      1 reply 0 retweets 7 likes
      François Chollet‏Verified account @fchollet Jul 12
      Replying to @svpino

      Same, I generally use the Functional API even for sequential-like models. By the way, you can actually use custom training/evaluation methods with Functional models, like this:pic.twitter.com/TJeUvgPphZ

      3:37 PM - 12 Jul 2021
      • 1 Retweet
      • 16 Likes
      • nkandy44 Liam O'Neil Mark Daoust olti Fahad Haleem Juri Wiens Christian Cahig Juan Carlos Piñeros vladdy
      1 reply 1 retweet 16 likes
        1. Santiago‏ @svpino Jul 12
          Replying to @fchollet

          Oh, interesting. This is exactly what I do, but since I’m subclassing Model I thought of it as Subclassing API and no longer Functional. Although it seems more like a combination of both.

          0 replies 0 retweets 6 likes
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