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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. This Tweet is unavailable.
    2. Ehsan Haghighat‏ @EhsanHaghighat Aug 6
      Replying to @fchollet

      I have had so much trouble with GradientTape for the so called Physics-Informed ML. Can we easily take differentiation of Keras layer outputs wrt inputs using GT?

      1 reply 0 retweets 1 like
    3. François Chollet‏Verified account @fchollet Aug 6
      Replying to @EhsanHaghighat

      Sure, you just need to make sure the inputs are tracked by the tape

      1 reply 0 retweets 0 likes
    4. Ehsan Haghighat‏ @EhsanHaghighat Aug 6
      Replying to @fchollet

      Is this supposed to work? If so, can you please point me to where I am doing wrong? I know that evaluating the network on tensor data works fine, but it is not a good setup for PINNs.pic.twitter.com/dgrtpgz5xb

      1 reply 0 retweets 0 likes
      François Chollet‏Verified account @fchollet Aug 6
      Replying to @EhsanHaghighat

      Try this.pic.twitter.com/1MKO5of7G0

      8:07 PM - 6 Aug 2021
      • 1 Like
      • Ehsan Haghighat
      2 replies 0 retweets 1 like
        1. François Chollet‏Verified account @fchollet Aug 6
          Replying to @fchollet @EhsanHaghighat

          François Chollet Retweeted François Chollet

          Note that `Input()` is for Functional model construction only. The Functional API is a graph-building API. See e.g.https://twitter.com/fchollet/status/1414665804570796038 …

          François Chollet added,

          François CholletVerified account @fchollet
          Tweetorial: the Functional API in Keras. Deep learning models are basically graphs of layers. Therefore, an intuitive API for defining deep learning models should be a *graph-definition API*. That's what the Functional API is: a Python-based DSL for graphs. It looks like this: pic.twitter.com/rthCY2Jqhz
          Show this thread
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        1. Ehsan Haghighat‏ @EhsanHaghighat Aug 6
          Replying to @fchollet

          Thanks - I know that, but I was looking for a way to avoid passing data during the construction of PINN model. The loss terms are quite complex in PINNs with multiple orders of differentiation, and the graph-based implementation allows better abstraction, I think.pic.twitter.com/5fu8h3kSft

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