This was also my experience. TensorFlow's model of constructing then evaluating a computation graph felt at odds with Python's principles. It made it extremely difficult to debug because you couldn't print tensors easily! It didn't feel like Python at all.
Also the API changed constantly so examples from docs or open source repos wouldn't work.
They also had that weird thing about all tensors having a unique global name. I remember I tried to evaluate a DQN network twice in the same script and it errored because of that.
It's somewhat vindicating to see many people in this thread shared my frustrations. Considering the impact of these technologies I think a documentary about why TensorFlow failed and PyTorch took off would be a great watch.
Also the API changed constantly so examples from docs or open source repos wouldn't work.
They also had that weird thing about all tensors having a unique global name. I remember I tried to evaluate a DQN network twice in the same script and it errored because of that.
It's somewhat vindicating to see many people in this thread shared my frustrations. Considering the impact of these technologies I think a documentary about why TensorFlow failed and PyTorch took off would be a great watch.