Anyways, this still sounds like a matter of taste more or less.
When you write in TensorFlow sometimes you feel that your model is behind a brick wall with several tiny holes to communicate over. Overall, the framework is more tightly integrated with Python language and feels more native most of the times. In PyTorch things are way more imperative and dynamic: you can define, change and execute nodes as you go, no special session interfaces or placeholders. All communication with outer world is performed via tf.Session object and tf.Placeholder which are tensors that will be substituted by external data at runtime. In TensorFlow you define graph statically before a model can run.
#ONLINE GRAPH BUILDER TENSORFLOW CODE#
TensorFlow follows ‘data as code and code is data’ idiom. Difference #1 - dynamic vs static graph definitionīoth frameworks operate on tensors and view any model as a directed acyclic graph (DAG), but they differ drastically on how you can define them. PyTorch also include several implementations of popular computer vision architectures which are super-easy to use. Documentation and official tutorials are also nice. PyTorch is relatively new compared to its competitor (and is still in beta), but it is quickly getting its momentum. You can find hundreds of implemented and trained models on github, start here. The framework is well documented and if the documentation will not suffice there are many extremely well-written tutorials on the internet.
Difference #0 - adoptionĬurrently, TensorFlow is considered as a to-go tool by many researchers and industry professionals. Now we are ready to explore some differences. It took more iterations to recover the exponent, but I am sure that the cause is I did not fiddle with optimiser’s parameters enough to reach comparable results. Loss function and exponent plots for TensorFlowĪs you can see, implementation in TensorFlow works too (surprisingly 🙃).
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