Computational graph approach for Torch about deepframeworks HOT 7 CLOSED

Nodes in Torch's nngraph are layers (or modules). The brick is layer. Nodes in TF/Theano graph are tensor operations. The brick is smaller.

The Python bridge looks pretty half-baked to me. I think that it's near impossible to have an ideal bridge (unless there's a magic function call that translates Python code to Lua code). If you write computational code in Python, you lose the performance of Torch; but you have to write computational code if you want to implement something new. Torch doesn't support symbolic programming.

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FWIW, I'm changing the format of this evaluation to a table view. This should make the comparison more detailed and fairer.

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You make valid points, so perhaps add those to the comparison in some way?

A table is good, but I would recommend a series of headings/points to cover, for example abstraction level, language support, packages/libraries etc. Then write some prose on each framework which justifies ratings. An example would be this review on API tools. I think this should give you the best of what you have now and a table view.

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Thanks for the suggestion. I've just updated the review.

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Thanks - much easier to see points of comparison now. Small final suggestion - a table of contents at the top using relative links so that people can see the different areas covered.

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Good suggestion. Will do.

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Added as you suggested. Thanks.

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