1. Comparison Between Deep Learning Packages
Yi Wu
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2. Outlines Background Caffe Theano TensorFlow Work Preliminary Results
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3. Background
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4. Background
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5. Background
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6. Caffe
From UC Berkeley
Written in C++
Python bindings
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7. Caffe Network structures are stored in .proto files
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8. Caffe
Model Zoo
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9. Caffe (+) Train models without writing any code
(+) Lots of pre-trained models, good for testing and fine-tuning
(+) Python interface
(-) Cumbersome for big networks
(-) No good for recurrent networks
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10. Theano From Yoshua Bengio’s group at University of Montreal
symbolic computation
compatible with Python
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11. Theano gradient on anything
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12. Theano black box magic http://www.marekrei.com/blog/theano-tutorial/
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13. Theano (+) Python + numpy (+) computation graph
(+) RNN fits nicely in computation graph
(-) Hard to manually optimize
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14. TensorFlow
From Google
Computation graphs
Scalable
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15. TensorFlow X h h2 y w_h2 w_h w_0
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16. TensorFlow X h h2 y w_h2 w_h w_0 define graph nodes
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17. TensorFlow X h h2 y w_h2 w_h w_0 define graph edges
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18. TensorFlow Tensorboard
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19. Work Image classification (CNN) data: PASCAL 2012 model: VGG16
21 classes
11540 images (random 1000)
model: VGG16
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20. Work Hardware data: 1000 images test for speed
CPU: I7 4GHz
GPU: GTX 1080
RAM: 32G
data: 1000 images
test for speed
CPU: Caffe vs Theano vs TensorFlow
GPU: Theano vs TensorFlow
test for easiness to use
installation
code
test & debug
visualization
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21. Preliminary Result
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22. Preliminary Result training with GPU predicted class takes 2 sec
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