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While we are at it, which framework is the easiest to tweak at the low-level, e.g. create modified LSTMs etc ?
For trying out deep learning, or build on existing models, pytorch or keras may be easier to grasp. But when making new models that involves a lot of math, the Theano/Tensorflow is more helpful IMO.
This article echoes my experience as well. I was working on some core NLP models for a larger tech company and wanted to experiment with Keras. I had my models designed within a day and training done within another and had amazing model perf. I was also told that doing it the real way using Tensorflow would be the way to go and I agree with that sentiment if my problem was Google scale which it wasn't. In fact I woul…
From my opinion: Getting started with Tensor Flow, and having a model designed within a day and training within another is also possible. This mostly depends on your model and your data, and (imho) not on the framework of choice. For all, Keras/PyTorch/Tensorflow, you'll need to learn the API - but if you have any ML background, that should be straight forward.
Though, debugging matters. In TF it is easy to get errors and spend a lot of time searching for them. In PyTorch it is straightforward. It matters the most when the network, or cost function, is not standard (think: YOLO architecture).
E.g. when I wanted to write some differentiable decision tree it took me way longer in TF (I already knew) than with PyTorch, having its tutorial on another pane.
I just like Tensorflow better. For building new models, the graph is complex and errors are unavoidable. There is a separate compile time for Tensorflow and errors will be found before the data come in. Tried pytorch before, the error messages are usually not helpful at all and often leads to clueless debugging for hours. For trying out deep learning, or build on existing models, pytorch or keras may be easier to gra…
I just like Tensorflow better. For building new models, the graph is complex and errors are unavoidable. There is a separate compile time for Tensorflow and errors will be found before the data come in. Tried pytorch before, the error messages are usually not helpful at all and often leads to clueless debugging for hours. For trying out deep learning, or build on existing models, pytorch or keras may be easier to gra…
For using models it may note matter that much (though, again read YOLO in TF and PyTorch and then decide which is cleaner :)).
For new models which go beyond a standard ConvNet/LSTM... well, PyTorch is heaven, Theano sounds like a torture.
I mean, just looking at the "getting started, 30 seconds to Keras"[0], there are so many magic strings and options. Of course, if one is well-versed in this domain, they make sense. But it's hard to grasp, and Keras is supposed to be the high-level one.
[0]: https://keras.io/
I just like Tensorflow better. For building new models, the graph is complex and errors are unavoidable. There is a separate compile time for Tensorflow and errors will be found before the data come in. Tried pytorch before, the error messages are usually not helpful at all and often leads to clueless debugging for hours. For trying out deep learning, or build on existing models, pytorch or keras may be easier to gra…
If a model involves a lot of math, is it more helpful to be able to debug it? What tf lacks is an intuitive debugging tool. I think this is where pytorch excels.
If data and model are mixed, it often resort to line-by-line debugging to zone out the real problem, which often takes more time.
This article echoes my experience as well. I was working on some core NLP models for a larger tech company and wanted to experiment with Keras. I had my models designed within a day and training done within another and had amazing model perf. I was also told that doing it the real way using Tensorflow would be the way to go and I agree with that sentiment if my problem was Google scale which it wasn't. In fact I woul…
> Kind of reminds me of assembly programmers that thought C wasn't for l33t 10xx pwner programmers. It's funny because this the same attitude C/C++ programmers have towards developers using other languages now...
Earlier quoted context omitted.
From my opinion: Getting started with Tensor Flow, and having a model designed within a day and training within another is also possible. This mostly depends on your model and your data, and (imho) not on the framework of choice. For all, Keras/PyTorch/Tensorflow, you'll need to learn the API - but if you have any ML background, that should be straight forward.
Yes, for all practical problems data is the biggest challenge. Though, debugging matters. In TF it is easy to get errors and spend a lot of time searching for them. In PyTorch it is straightforward. It matters the most when the network, or cost function, is not standard (think: YOLO architecture). E.g. when I wanted to write some differentiable decision tree it took me way longer in TF (I already knew) than with PyTo…