81–90 of 211 posts
> The TensorFlow Python API requires Python 2.7 The whole scientific Python stack is available for Python 3. This seems like a somewhat backwards thing to do -- or perhaps the requirement is intended to mean at least 2.7? Edit: added context Edited again: More careful wording
We're looking to support Python 3 -- there are a few changes we are aware of that are required, and we welcome contributions to help! Tracking here: https://github.com/tensorflow/tensorflow/issues/1
If we could stop using the term Neural Networks in relation to computing, that would be great. Computers don't have neurons.
There are artificial and biological neural networks. The artificial one is based upon the biological one. Therefore dropping the 'artificial' or 'biological' seems fine to me.
> The TensorFlow Python API requires Python 2.7 The whole scientific Python stack is available for Python 3. This seems like a somewhat backwards thing to do -- or perhaps the requirement is intended to mean at least 2.7? Edit: added context Edited again: More careful wording
This "backwards" meme doesn't make any sense. Python 2 and Python 3 are different languages that happen to share the same name, a lot of syntax, and near-source compatibility, but they've been developed concurrently for ten years now. It's kind of like C and C++. If the current Python 2 team (which is part of the Python Software Foundation) abandons it, probably somebody else will take over maintenance, because it se…
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> with no investment in the outcome other than graduating Come on. Grad students are motivated by advancing the field. ... are they not? Maybe you can also be saying that the ones who write libraries are motivated only by graduating.
Sure, we all start out that way. But by the 3rd or 4th year, you frequently just want to write that thesis and get on to the next thing because you've already advanced the field as much as you're going to as a grad student. I have a large package of code on sourceforge that consists of my grad school and postdoctoral efforts. If I compare its style to what 2 decades in the industry since has taught me, it's laughable…
But I do see your point. Google obviously has a lot more manpower to spend on this, so it might be a better bet in the long run.
It's also worth comparing this to a few similar projects that have been announced recently: MXNet (http://mxnet.readthedocs.org/en/latest/), Chainer (http://chainer.org/) and CGT (http://rll.berkeley.edu/cgt/). And Theano of course, which has been the flag-bearer of the computational graph approach in deep learning for many years.
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Sure, now. 4 years ago, not so much. One might even conclude that one person inside Google had written a brawny and influential paper that was right in a lot of ways, but wimpily dead-wrong to apply to GPUs.
Might one conclude that getting hung up on one opinion from a large group of people projected forward in time is... not very useful?
Now we all must own our choices and one was very silly, stupid, and naive to blindly accept a position at Google, but everyone makes mistakes, no?
Further, TensorFlow looks fantastic but given that even just a year ago, when one was recruited to return to Google for !(Deep Learning) but for something which also happened to be another braindead obvious choice to run on GPUs, one was informed said viewpoint on GPUs was still in effect and declined.
So what I'd really love to know is how the message finally sunk in? I'm betting there's a really great story here.
This is really significant. At this moment in history, the growth of computer power has made a bunch of important signal-processing and statistical tasks just feasible, so we are seeing things like self-driving cars, superhuman image recognition, and so on. But it's been very difficult to take advantage of the available computational power, because it's in the form of GPUs and clusters. TensorFlow is a library design…
I think that's fantastic and wish academia went that way too (yeah right).
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> Tensors are the future; matrices are going to look so old in a few years. I am honestly curious about this point of view. Is there any example where actual multidimensional tensor have any relevance? What I mostly see around is just standard linear algebra operations on matrices and vectors lifted to higher-dimensional tensors point-wise (for instance applying a certain operation to all 2-dimensional subtensors of…
In my domain (finance) correlations between vectors are unstable but (maybe) dependent on cross-sectional relationships in the problem space. Some of the mathematics behind elastic body deformation (car tyres in in mechanical engineering, fluid dynamics in weather forecasting) have high applicability. Tensors are required. It's true that tensors are hard to reason about - they overclock my brain most of the time - bu…
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What about Torch or Theano which allow you to use multiple GPUs and clusters? they also have a wide array of libraries which allows you to extend capabilities (itorch etc.). torch is also very fast, most parts written in C so I don't know if Tensorflow would be really that fast compared to existing librairies. One thing find I found interesting is the ability to use the software you designed in research directly in p…
As far as I can tell, this may not be faster than theano/torch, but it doesn't sound like it'll be slower. It takes a similar approach of sending all the big computation stuff out to efficient implementations. The "slow" language code is simply building up a graph of work to be done by the efficient implementations.
Right now, Torch/Caffe adoption is really good with lots of research code being released for it (even from within Google DeepMind). So it will be interesting to see how this catches up amongst the research and developer community.
Are there any major conceptual differences to Theano? Not that I wouldn't appreciate a more polished, well funded competitor in the same space. It looks like using TensorFlow from Python will feel quite familiar to a Theano user, starting with the separation of graph building and graph running, but also down into the details of how variables, inputs and 'givens' (called feed dicts in tensorflow) are handled.
I'm looking at the RNN implementation right now ( https://github.com/tensorflow/tensorflow/blob/master/tensorf... ). It looks like the loop over the time frames is actually in Python itself. for time, input_ in enumerate(inputs): ... This confuses me a bit. Maybe the shape is not symbolic but must be fixed. I also haven't seen some theano.scan equivalent. Which is not needed in many cases when you know the shape in a…
It seems like in TensorFlow you can say:
import tensorflow as tf
sess = tf.InteractiveSession() # magic incantation
state = init_state = tf.Variable(1) # initialise a scalar variable
states = []
for step in range(10):
# this seems to define a graph that updates `state`:
state = tf.add(state,state)
states.append(state)
sess.run(tf.initialize_all_variables())
print sess.run(states)
>>> [2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]