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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. Patrick Rodriguez‏ @stratospark 20 Apr 2017
      Replying to @vishnuvig @fkratzert and

      I tried this on my system, up'd to Keras 2. pytorch: 80s/epoch. keras 2: 93s/epoch. 1 worker each. w/o flip/normalize for fairer comparison

      2 replies 1 retweet 1 like
    2. François Chollet‏Verified account @fchollet 20 Apr 2017
      Replying to @stratospark @vishnuvig and

      You can get even better performance by removing the overhead of feed_dict. Simply use a TF queue and build your Keras model on top of that.

      3 replies 2 retweets 7 likes
    3. mat kelcey‏ @mat_kelcey 20 Apr 2017
      Replying to @fchollet @stratospark and

      a benchmark using feed_dict ? o_O

      1 reply 1 retweet 2 likes
    4. mat kelcey‏ @mat_kelcey 20 Apr 2017
      Replying to @mat_kelcey @fchollet and

      having said that, using queues needs to be made a LOT simpler, almost no examples i see on GitHub use them, it's always feed_dict

      3 replies 0 retweets 7 likes
    5. mat kelcey‏ @mat_kelcey 20 Apr 2017
      Replying to @mat_kelcey @fchollet and

      hell, probably all MY tf code on GitHub uses feed_dict :)

      2 replies 2 retweets 3 likes
    6. hardmaru‏ @hardmaru 20 Apr 2017
      Replying to @mat_kelcey @fchollet and

      yeah... can't really blame'em. all of my code use feed_dicts too. :-)

      2 replies 0 retweets 3 likes
    7. François Chollet‏Verified account @fchollet 20 Apr 2017
      Replying to @hardmaru @mat_kelcey and

      The overhead of feed_dict is small. However, making input preprocessing blocking instead of having it happen in parallel to execution ->🤦‍♂️

      3 replies 1 retweet 4 likes
    8. Vishnu Subramanian‏ @vishnuvig 20 Apr 2017
      Replying to @fchollet @hardmaru and

      I am using keras.preprocessing.image ,is there a way I can use parallel execution in it. Or should I use directly tensorflow directly in it

      1 reply 0 retweets 0 likes
    9. François Chollet‏Verified account @fchollet 20 Apr 2017
      Replying to @vishnuvig @hardmaru and

      Just use `pickle_safe=True` and `num_workers=4` (for instance) in your call to `fit_generator`. That is all.

      2 replies 0 retweets 6 likes
    10. Vishnu Subramanian‏ @vishnuvig 20 Apr 2017
      Replying to @fchollet @hardmaru and

      That's cool will try it and update the blog

      1 reply 1 retweet 0 likes
      François Chollet‏Verified account @fchollet 20 Apr 2017
      Replying to @vishnuvig @hardmaru and

      Better to use TF input queues, though

      8:10 PM - 20 Apr 2017
      • 1 Retweet
      • alejandro vergara
      1 reply 1 retweet 0 likes
        1. Vishnu Subramanian‏ @vishnuvig 20 Apr 2017
          Replying to @fchollet @hardmaru and

          By using pickle_safe = True and nb_worker = 6 the performance improved to 1 min 40 sec. Have updated the blog accordingly.

          0 replies 0 retweets 1 like
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