DeepMind Open Sources 'Sonnet' Library For Easier Creation Of Neural Networks (fossbytes.com) 17
"We are very excited about contributions from the community," announced Alphabet's DeepMind, open sourcing a new library to make it easier to build complex TensorFlow neural networks. An anonymous reader writes:
"DeepMind foresees Sonnet to be used by the community as a research propellant," reports FossBytes. "Also, it would allow easy sharing of other models created by DeepMind with the community." Sonnet uses an object-oriented approach, a recent blog post explained, pointing to more details on GitHub. "The main principle of 'Sonnet' is to first construct Python objects which represent some part of a neural network, and then separately connect these objects into the TensorFlow computation graph."
DeepMind sees this as part of their broader commitment to open source AI research. "In recent months we've also open-sourced our flagship platform DeepMind Lab, and are currently working with Blizzard to develop an open source API that supports AI research in StarCraft II."
DeepMind sees this as part of their broader commitment to open source AI research. "In recent months we've also open-sourced our flagship platform DeepMind Lab, and are currently working with Blizzard to develop an open source API that supports AI research in StarCraft II."
Easier for data scientists (Score:1)
After recently diving into machine learning and playing with the available libraries out there. IMO what is really needed is libraries that are developer friendly, not data scientist friendly. That's when we will see a lot more developers starting to apply this to more real world problems.
I know enough to know that I don't need to know that much about the algorithms involved to tweak them towards an end goal. I just need to know ok this knob moves it this way, this algorithm is best for this type of prob
Ditto. Much like assembly to C to C# (Score:3)
Agreed. It seems we've gotten over a hump going from machine learning etc being a research area to where it's now usable for real-world applications. However, it's currently usable only for specialists. A major breakthrough will be when it's abstracted and packaged to be usable by ordinary developers.
Once upon a time, to write programs for a particular CPU, you had to know about the details of that particular CPU hardware, its registers and all that, and write in the appropriate asssembly language. Later,
Re: (Score:2)
And soon, there won't be programmers anymore because computers will program themselves :)
You only have to write the program's specification (a square mile of UML diagrams will do).
Re: (Score:2)
Choosing the values might be hard?
Not only there's the problems of discarding, clamping, correcting data (if you remember "hide the decline", maybe that's stuff you will have to do) but I wonder if you'll end up doing something silly. E.g., your system ends up increasing the production of men's underwear when the number of pirate attacks is low. Then next year a outlying bout of 1000 pirate attacks happens, your program falls apart but before the underwear company notices they shouldn't blindly trust what c
Got one thing right... (Score:2)
Re: (Score:2, Interesting)
I'm pretty avid about keeping in the loop with Machine Learning and I don't know TensorFlow well enough to understand the benefits of this.
I'll say that TensorFlow by itself without TFlearn or Keras is pretty obtuse, even for someone who understands the theory fairly well. I can fluently use words like mini-batch normalization, max pooling, drop out, ReLU, and convolution kernel in a conversation, but even the basic MLP TensorFlow MNIST example isn't exactly easy to understand (and yes, I know the linear al
Error During Build (Score:2)
I'd like to try this but the build is failing on a newly updated ubuntu system.
bazel build --config=opt :install
C++ compilation of rule '//sonnet/cc/kernels:resampler_op' failed
Has anyone had success following the build instructions here: https://bazel.build/versions/m... [bazel.build]