Tensorflow 2.0 Beta 0

https://github.com/tensorflow/tensorflow/releases/tag/v2.0.0-beta0

by Gimpei • 7 years ago

109 43 7 years ago

I am very happy that Google has realized the importance of usability. Hopefully that comes with concomitant improvements in the tf documentation, which, while thorough is completely unusable and lacks good examples for complex things.

levesque2• 7 years ago

Might give it another try, but my latest incursion in the Tensorflow universe did not end pleasantly. I ended up recoding everything in Pytorch, took me less than a day to do the stuff that took me more than a week in TF. One problem is that there are too many ways to do the same thing in TF and it's hard to transition from one to the other.

m0zg3• 7 years ago

Yeah, the only reason to use TF is really its deployment friendliness. If PyTorch addressed that more comprehensively, there'd be no good reason to use TF at all. For research PyTorch blows TF out of the water completely, and it's been that way for years, ever since it came out.

forgotmyhnacc• 7 years ago

What are you looking for in deployment friendliness? There's TorchScript to run your code faster (which is a work in progress)

sandGorgon• 7 years ago

What do you think of Keras in this space ? Because TF 2.0 is entirely keras based.

https://medium.com/tensorflow/standardizing-on-keras-guidanc...

sytelus• 7 years ago

This is one thing that confuses me. Why Keras is still a separate brand? Why everything isn't under just tensorflow namespace instead of having to do tf.keras all the time. I really wish tf just had one API and just one thing to learn.

levesque2• 7 years ago

Keras is part of the problem for me. It is rather rigid and it's hard to get around. Works super well for the regular use case. On the other hand, when you want to start doing custom stuff, it's hell.

NickHoff• 7 years ago

Maybe I'll give TF another try, but right now I'm really liking PyTorch. With TensorFlow I always felt like my models were buried deep in the machine and it was very hard to inspect and change them, and if I wanted to do something non-standard (which for me is most of the time) it was difficult even with Keras. With PyTorch though, I connect things however how I want, write whatever training logic I want, and I feel like my model is right in my hands. It's great for research and proofs-of-concept. Maybe for production too.

p1esk6• 7 years ago

I just hope they don't screw it up in the process of integrating PT with Caffe2.

strebler• 7 years ago

TF's deprecation velocity was way too high for my taste. Things we wrote would stop working randomly with their updates. I feel very similar to you about the models being "buried too deep" in their (ever-changing) machine. I much preferred how easy it was to hack Caffe V1 (once you got past the funky names, etc).

These days, I really like mxnet. Torch was a disaster, but Pytorch is much better. It's not bad in production, definitely my #2.

panpanna6• 7 years ago

TF's deprecation velocity

That's Google on a nutshell. In fact, they may drop TF altogether next month. You never know ...

chipotle69• 7 years ago

Test

iamcreasy• 7 years ago

I am curious, what do you like about mxnet?

panpanna6• 7 years ago

Can't explain it but for some reason Tensorflow never felt "right" to me, even work keras.

Pytorch on the other hand feels so much more natural...

credit_guy3• 7 years ago

Maybe I'm not up to speed with the latest PyTorch, but to me Keras feels much more natural. In Keras if you want to define a deep learning network, then you just do that, you specify the first layer, the second layer, etc, then you calibrate over some test and validation samples, using a certain flavor of gradient descent, for a given loss function. In PyTorch, you have to define a class, with a constructor, some method called "forward", I don't know, maybe if I follow an example to the end I get the hang of it. My problem is that I don't want to write object oriented programming, I want to do machine learning. Keras doesn't force me to know what a class it, or what it means to inherit from nn.Module, or that a constructor in python needs to contain 'self' as a variable. PyTorch, at least the exmples I saw online want me to do just that, and that's a turnoff.

On the other hand, in Keras I can't (easily) change the architecture of a learner after I defined it. I can't prune some nodes and split others, maybe that's easy in PyTorch. If that's the case, I'll take a second look. Until then, when I have some time, I'm really tempted to invest some time in MXNet, as the book "Dive into Deep Learning" appears to be quite good.

flensortow• 7 years ago

Any news on Swift for Tensorflow?

I’m skeptical of how much practical benefit it will provide but still willing to take a look at it.

There doesn’t seem to be any mention of it here.

panpanna6• 7 years ago

From what I understand this is mostly because they hired the Swift guy.

I understand the benefits compared to Python (although I would have preferred Go or Kotlin). But what happens when the guy eventually moves on in a year or two?

favorited3• 7 years ago

Google's making a big investment in Swift, so if Chris left and they were interested in continuing to support it they shouldn't have a problem.

I've gone to Swift on the Server conferences hosted/sponsored by Google, their (non-TF) Swift teams are building some cool Swift tools, etc.

mark_l_watson• 7 years ago

I have spent a few evenings playing with early releases. I like the ‘turtles all the way down’ idea, but I am waiting to see more mature releases. I have spent much more time with TensirFlowJS that works well, has many great examples.

nickserv• 7 years ago

Nice to see the project moving along, I'm just getting started with the basics for a way finding application and will probably start off with version 2 then.

Hopefully by the time stable comes around I'll be near production ready as well.

A bit off-topic, but does TF or pyTorch work nicely with AMD GPUs?

I'd rather not have to deal with Nvidia's blob drivers if at all possible.

p1esk6• 7 years ago

does TF or pyTorch work nicely with AMD GPUs?

No.

hn2017• 7 years ago

Does anyone know when there'll be updated Coursera (or other sites) courses with TF 2.0?

wodenokoto• 7 years ago

They have one on udacity

Tensorflow 2.0 Beta 0

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