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At your contrary, I love the graph. However, migrating to a keras-like approach, you can work thinking about "objects with variables inside" and then the graph
by me2too 8y ago
At your contrary, I love the graph.
However, migrating to a keras-like approach, you can work thinking about "objects with variables inside" and then the graph will be built for you by the abstraction introduced by `tf.keras.Model`.
However, for automatic differentiation, graph is always required (as you can see from the example that uses eager)
- amelius 8y ago> At your contrary, I love the graph. Can you explain what you like so much about manipulating graphs, over just writing statements like z= x⋆y, where ⋆ is some tensor operation? Really, the graph makes me feel like I'm stacking Lego bricks with chopsticks, rather than with my bare hands ;)
- me2too 8y agoThe fact that I have all the computation described in a coherent manner, in something that's agnostic to the language I'm using. The same description (the graph) can be taken and used in every other language. I can move a trained model to production just picking a single file (a `tf.train.SavedModel` IIRC), give it a tag and I'm ready to go, with different models with a native support for the "tagging" (hence the model versioning).
- amelius 8y agoWhether that's an advantage depends on your perspective, because what has really happened is that you have now created a new language inside the original language.