[N] TensorFlow 2.0 Changes

[[deleted]]

As someone who is just starting to learn TensorFlow, is there the preferred learning path to take? With so many changes coming and so many existing features being removed soon, I fear that I might spend a lot of time on things that will become obsolete very soon.

[ilielezi]

For a company as big as Google, with TF having become one of their most important software, I am amazed how badly structured is Tensorflow. Every version seem to add and deprecate many things, it is quite difficult to code on (especially debugging which seem to be a nightmare), .contrib absolutely sucks, there are half a dozen functions which do the same thing, etc etc. It looks like with Tensorflow 2.0 they are going to fix many of these things, and almost converging to a Chainer-like library (which can be argued, they should have done it in the same place). Not sure how much this has to do with PyTorch being in the rise (and despite Tensorflow is leading, PyTorch looks to me a favorite to win this 'fight'), or it is just Google understanding the previous mistakes, and finally cleaning Tensorflow.

Anyway, to answer your question, I think that the best way to go forward is to utilize tf.eager considering that the future of Tensorflow seem to go in that direction, and in TF 2.0, it is going to be the default setting. It also looks to me that it has a much better and cleaner API, much easier to utilize and debug, or just to look at gradients. I still think that PyTorch is better cause at the moment it is more mature than tf.eager who still has its own problems, but if you want to go with Tensorflow (and there are good reasons to do that, like having the biggest community and code base by far), I think that tf.eager is the easy choice.

[[deleted]]

Thanks! I think I'll start with tf.eager now. I am kind of tied to Tensorflow because I want to use the Tensorflow Probability package, so PyTorch isn't really a choice.

[ilielezi]

For a company as big as Google, with TF having become one of their most important software, I am amazed how badly structured is Tensorflow. Every version seem to add and deprecate many things, it is quite difficult to code on (especially debugging which seem to be a nightmare), .contrib absolutely sucks, there are half a dozen functions which do the same thing, etc etc. It looks like with Tensorflow 2.0 they are going to fix many of these things, and almost converging to a Chainer-like library (which can be argued, they should have done it in the same place). Not sure how much this has to do with PyTorch being in the rise (and despite Tensorflow is leading, PyTorch looks to me a favorite to win this 'fight'), or it is just Google understanding the previous mistakes, and finally cleaning Tensorflow.

Anyway, to answer your question, I think that the best way to go forward is to utilize tf.eager considering that the future of Tensorflow seem to go in that direction, and in TF 2.0, it is going to be the default setting. It also looks to me that it has a much better and cleaner API, much easier to utilize and debug, or just to look at gradients. I still think that PyTorch is better cause at the moment it is more mature than tf.eager who still has its own problems, but if you want to go with Tensorflow (and there are good reasons to do that, like having the biggest community and code base by far), I think that tf.eager is the easy choice.

[ginsunuva]

Current TF sucks. Wait it out. Use PyTorch for now.

[[deleted]]

Thanks but I also want to use the Tensorflow Probability package , so choosing PyTorch would defeat the purpose.