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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.
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?
Does anyone know when there'll be updated Coursera (or other sites) courses with TF 2.0?
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.
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?
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.
Earlier quoted context omitted.
Keras is a high level API that can use multiple backends. So it makes sense for them to remain separated.
I haven't used TF much lately, but the last time I looked at TF2 it felt like they are making it harder to build low level api models.
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…
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 produc…
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…
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...
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.
Earlier quoted context omitted.
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...
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 met…
I'm not sure what you mean here, because only PT lets you change architecture after you define it, while TF/Keras uses a static precompiled graph. Now that's changing with eager mode, but that used to be the main advantage of PT.
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.
No.
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…
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 produc…