NumPy-style broadcasting for R TensorFlow users

We establish, train, and release TensorFlow designs from R. However that does not imply we do not use documents, post, and examples composed in Python. We search for particular performance in the main TensorFlow API docs; we get motivation from other individuals’s code.

Depending upon how comfy you are with Python, there’s an issue. For instance: You’re expected to understand how broadcasting works. And maybe, you ‘d state you’re slightly knowledgeable about it: So when ranges have various shapes, some aspects get duplicated till their shapes match and … and isn’t R vectorized anyhow?

While such a worldwide idea might operate in basic, like when skimming a post, it’s inadequate to comprehend, state, examples in the TensorFlow API docs. In this post, we’ll attempt to come to a more specific understanding, and inspect it on concrete examples.

Mentioning examples, here are 2 encouraging ones.

Broadcasting in action

The very first usages TensorFlow’s matmul to increase 2 tensors. Would you like to think the outcome– not the numbers, however how it happens in basic? Does this even run without mistake– should not matrices be two-dimensional ( rank -2 tensors, in TensorFlow speak)?

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