[cttet]

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 grasp. But when making new models that involves a lot of math, the Theano/Tensorflow is more helpful IMO.

[stared]

It is an interesting perspective, but my experience is exactly the opposite. In Theano debugging was awful. TF felt like a breeze until it didn't. When I jumped on PyTorch - it TF started feeling confusing by comparison. Errors exactly in the defective lines, possibility to print everywhere (or using any other kind of feedback / logging intermediate results).

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.

[cttet]

I am not sure what is your programming style in Pytorch. As people recommended and in most tutorials I see the sequential approach, where a small mistake of data preprocessing would lead to clueless errors in a completely irrelevant line.

YOLO is a quite standard feed-forward model in my opinion. I mean the math part, which I am more concerned with.

I have never used Theano before, my idea from it is that Tensorflow followed its static graph approach.

[stared]

Did you read cost function of YOLO in both languages?

[v4r]

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.

[cttet]

You don't often need to debug, especially if the model can be checked on compile time. Think static types for programming. For Tensorflow all the data types and tensor dimensions are checked before loading any data, it the math is derived correctly then it is not necessary to even debug.

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.