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This suggested edit was approved and applied to the post 8 months ago by Alexei‭.

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Is it okay to use python operators for tensorflow tensors?
  • **TL;DR**
  • Is `(a and b)` equivalent to `tf.logical_and(a, b)` in terms of optimization and performance? (`a` and `b` are tensorflow tensors)
  • **Details**:
  • I use python with tensorflow. My first priority is to make the code run fast and my second priority is to make it readable. I have working and fast code that, for my personal feeling, looks ugly:
  • @tf.function
  • # @tf.function(jit_compile=True)
  • def my_tf_func():
  • # ...
  • a = ... # some tensorflow tensor
  • b = ... # another tensorflow tensor
  • # currently ugly: prefix notation with tf.logical_and
  • c = tf.math.count_nonzero(tf.logical_and(a, b))
  • # more readable alternative: infix notation:
  • c = tf.math.count_nonzero(a and b)
  • # ...
  • The code that uses [prefix notation][1] works and runs fast, but I don't think it's very readable due to the prefix notation (it's called prefix notation, because the name of the operation `logical_and` comes before the operands `a` and `b`).
  • Can I use [infix notation][2], i.e. the alternative at the end of above code, with usual python operators like `and`, `+`, `-`, or `==` and still get all the benefits of tensorflow on the GPU and compile it with XLA support? Will it compile to the same result?
  • The same question applies to unary operators like `not` vs. `tf.logical_not(...)`.
  • [1]: https://en.wikipedia.org/wiki/Polish_notation
  • [2]: https://en.wikipedia.org/wiki/Infix_notation
  • <sub>This question was crossposted at
  • https://stackoverflow.com/questions/77045818/is-it-okay-to-use-python-operators-for-tensorflow-tensors .</sub>
  • TL;DR
  • -
  • Is `(a and b)` equivalent to `tf.logical_and(a, b)` in terms of optimization and performance? (`a` and `b` are tensorflow tensors)
  • Details
  • -
  • I use Python with Tensorflow. My priorities are
  • 1. Make the code run fast
  • 2. Make it readable.
  • I have working and fast code that, for my personal feeling, looks ugly:
  • ```python
  • @tf.function
  • # @tf.function(jit_compile=True)
  • def my_tf_func():
  • # ...
  • a = ... # some tensorflow tensor
  • b = ... # another tensorflow tensor
  • # currently ugly: prefix notation with tf.logical_and
  • c = tf.math.count_nonzero(tf.logical_and(a, b))
  • # more readable alternative: infix notation:
  • c = tf.math.count_nonzero(a and b)
  • # ...
  • ```
  • The code that uses [prefix notation][1] works and runs fast, but I don't think it's very readable due to the prefix notation (it's called prefix notation, because the name of the operation `logical_and` comes before the operands `a` and `b`).
  • Can I use [infix notation][2], i.e. the alternative at the end of above code, with usual python operators like `and`, `+`, `-`, or `==` and still get all the benefits of tensorflow on the GPU and compile it with XLA support? Will it compile to the same result?
  • The same question applies to unary operators like `not` vs. `tf.logical_not(...)`.
  • [1]: https://en.wikipedia.org/wiki/Polish_notation
  • [2]: https://en.wikipedia.org/wiki/Infix_notation
  • <sub>This question was crossposted at
  • https://stackoverflow.com/questions/77045818/is-it-okay-to-use-python-operators-for-tensorflow-tensors .</sub>

Suggested 8 months ago by meta user‭