Disclaimer, I work for Google, but not on Tensorflow. I agree that in TF 1.x the ergonomics are/were bad, but things improved considerably in TF 2.x. In the ca

Disclaimer, I work for Google, but not on Tensorflow. I agree that in TF 1.x the ergonomics are/were bad, but things improved considerably in TF 2.x. In the case you don't like that either, the tf.keras package offer another more friendlier option that integrates well with the other tf packages (e.g. tf.data, tf.estimators, ...). Finally, I believe the decision also depends on your use case: If you are just experimenting, maybe PyTorch gets to your results faster. However, I also I think that being able to use TFX (https://www.tensorflow.org/tfx https://www.tensorflow.org/tfx) seamlessly saves you a lot of time when you need to put your models in production.