New Editor – Tam T. Nguyen, Kaggle Grandmaster

I am happy to announce that we have a new editor, Tam T. Nguyen, joining Kaggler.com.

Tam is a Competition Grandmaster at Kaggle. He won the 1st prizes at KDD Cup 2015, IJCAI-15 repeat buyer competition, and Springleaf marketing response competition.

Currently, he is Postdoctoral Search Fellow at Ryerson University in Toronto, Canada. Prior to that, he was Data Analytics Project Lead at I2R A*STAR. He earned his Ph.D. in Computer Science from NTU Singapore. He’s originally from Vietnam.

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Kaggler. Data Scientist.

Keras Backend Benchmark: Theano vs TensorFlow vs CNTK

Inspired by Max Woolf’s benchmark, the performance of 3 different backends (Theano, TensorFlow, and CNTK) of Keras with 4 different GPUs (K80, M60, Titan X, and 1080 Ti) across various neural network tasks are compared.

For the performance of TensorFlow and CNTK with K80, the numbers reported at Max Woolf’s benchmark are used.

Conclusion

  • The accuracies of Theano, TensorFlow and CNTK backends are similar across all benchmark tests, while speeds vary a lot.
    • Theano is significantly (up to 50 times) slower than TensorFlow and CNTK.
    • Between TensorFlow and CNTK, CNTK is a lot (about 2 to 4 times) faster than TensorFlow for LSTM (Bidirectional LSTM on IMDb Data and Text Generation via LSTM), while speeds for other type of neural networks are close to each other.
  • Among K80, M60, Titan X and 1080 Ti GPUs:
    • 1080 Ti is the fastest.
    • K80 is the slowest.
    • M60 is faster than K80 and comparable to Titan X and 1080 Ti.
    • Theano is significantly (up to 14 times) faster on 1080 Ti than on Titan X, while the improvements for TensorFlow and CNTK are moderate.

Detailed results are available at https://github.com/szilard/benchm-dl/blob/master/keras_backend.md

Kaggler. Data Scientist.