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Can you speak for the implementing of algorithms in TensorFlow? My impression is that it's still quite difficult to introduce something interesting that doesn't

Can you speak for the implementing of algorithms in TensorFlow? My impression is that it's still quite difficult to introduce something interesting that doesn't already exist. This is basically the case for all the frameworks, but I've found that even though Torch is written Lua, there is much much less abstraction.

pigscantfly 10y ago
I can't speak for TensorFlow, but I've personally found Theano to be the most easily extensible among the other widespread deep/reinforcement/gradient learning libraries (Caffe, Torch, Keras, Chainer). There does seem to be a performance hit between Theano and Caffe/Torch, but it's great for prototyping.
sja 10y ago
Implementing a new operation in TensorFlow does require a fair amount of overhead, and you'll need to know CUDA if you want to create something that works on (NVIDIA) GPUs. The "Creating an Op" how-to[1] goes over the basic steps necessary to implement an operation, but there are a couple of things missing. Notably, proper documentation for the various functions and classes used when writing operations, as well as information on writing (and registering) a Python wrapper for additional functionality. All this said, if you want to commit your work to the GitHub master, the Google team working on the repository does an excellent job of walking contributors through the steps necessary to get their work over the hump. [1]: https://www.tensorflow.org/versions/master/how_tos/adding_an_op/index.html https://www.tensorflow.org/versions/master/how_tos/adding_an...
clickok 10y ago
TensorFlow makes some things difficult in truly baffling ways. For example, the last time I checked there's no built-in way to get gradients w/r/t say a single weight, rather you get a gradient sum. So if you want to do anything with the Jacobian (e.g. contractive autoencoders) you are better off using Theano.

pigscantfly 10y ago

I can't speak for TensorFlow, but I've personally found Theano to be the most easily extensible among the other widespread deep/reinforcement/gradient learning libraries (Caffe, Torch, Keras, Chainer). There does seem to be a performance hit between Theano and Caffe/Torch, but it's great for prototyping.

sja 10y ago

Implementing a new operation in TensorFlow does require a fair amount of overhead, and you'll need to know CUDA if you want to create something that works on (NVIDIA) GPUs. The "Creating an Op" how-to[1] goes over the basic steps necessary to implement an operation, but there are a couple of things missing. Notably, proper documentation for the various functions and classes used when writing operations, as well as information on writing (and registering) a Python wrapper for additional functionality. All this said, if you want to commit your work to the GitHub master, the Google team working on the repository does an excellent job of walking contributors through the steps necessary to get their work over the hump. [1]: https://www.tensorflow.org/versions/master/how_tos/adding_an_op/index.html https://www.tensorflow.org/versions/master/how_tos/adding_an...

clickok 10y ago

TensorFlow makes some things difficult in truly baffling ways. For example, the last time I checked there's no built-in way to get gradients w/r/t say a single weight, rather you get a gradient sum. So if you want to do anything with the Jacobian (e.g. contractive autoencoders) you are better off using Theano.