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Q&A Best practices to write functions for both execution modes in Tensorflow, eager and graph mode

I regularly run into the problem that I have a Python function that I want to use in both, eager and graph execution mode. I therefore have to adjust the code so that it can handle both situations....

1 answer  ·  posted 1y ago by daniel_s‭  ·  edited 1y ago by daniel_s‭

#5: Post edited by user avatar daniel_s‭ · 2023-09-11T08:27:02Z (over 1 year ago)
  • Best practices to write functions for both, modes, eager and graph in Tensorflow
  • Best practices to write functions for both execution modes in Tensorflow, eager and graph mode
#4: Post edited by user avatar meta user‭ · 2023-09-09T18:18:53Z (over 1 year ago)
reword title, separate the two examples, convert multiline comment into single-line comment (changed code formatting), removed double occurence of similar meaning (helpful))
  • Tensorflow: Best practices to write functions for both, modes, eager and graph
  • Best practices to write functions for both, modes, eager and graph in Tensorflow
  • I regularly run into the problem that I have a Python function that I want to use in both, eager and graph execution mode. I therefore have to adjust the code so that it can handle both situations. Here are two examples:
  • ```python
  • import tensorflow as tf
  • def lin_to_db(x: float | tf.Tensor) -> float | tf.Tensor:
  • """ convert signal to noise ratio (SNR) from linear to dB """
  • if tf.is_tensor(x):
  • return tf.math.log(x) * (10. / tf.math.log(10.))
  • else:
  • return math.log10(x) * 10.
  • def cast_to_int_if_eager(x: tf.Variable) -> int | tf.Variable:
  • return int(x) if tf.executing_eagerly() else x
  • ```
  • Are there best practices for such functions? Or maybe helpful predefined functions from tensorflow which make my life easier?
  • I regularly run into the problem that I have a Python function that I want to use in both, eager and graph execution mode. I therefore have to adjust the code so that it can handle both situations. Here are two examples:
  • ```python
  • import tensorflow as tf
  • ```
  • ```python
  • def lin_to_db(x: float | tf.Tensor) -> float | tf.Tensor:
  • # convert signal to noise ratio (SNR) from linear to dB
  • if tf.is_tensor(x):
  • return tf.math.log(x) * (10. / tf.math.log(10.))
  • else:
  • return math.log10(x) * 10.
  • ```
  • ```python
  • def cast_to_int_if_eager(x: tf.Variable) -> int | tf.Variable:
  • return int(x) if tf.executing_eagerly() else x
  • ```
  • Are there best practices for such functions? Or maybe helpful predefined functions from Tensorflow?
#3: Post edited by user avatar Alexei‭ · 2023-09-08T16:33:51Z (over 1 year ago)
minor fixes + improved code formatting
  • I regularly run into the problem that I have a python function which I want to use in both, eager and graph execution mode. I therefor have to adjust code so that it can handle both situations. Here are two examples:
  • import tensorflow as tf
  • def lin_to_db(x: float | tf.Tensor) -> float | tf.Tensor:
  • """ convert signal to noise ratio (SNR) from linear to dB """
  • if tf.is_tensor(x):
  • return tf.math.log(x) * (10. / tf.math.log(10.))
  • else:
  • return math.log10(x) * 10.
  • def cast_to_int_if_eager(x: tf.Variable) -> int | tf.Variable:
  • return int(x) if tf.executing_eagerly() else x
  • Are there best practices for such functions? Or maybe helpful predefined functions from tensorflow which make my life easier?
  • I regularly run into the problem that I have a Python function that I want to use in both, eager and graph execution mode. I therefore have to adjust the code so that it can handle both situations. Here are two examples:
  • ```python
  • import tensorflow as tf
  • def lin_to_db(x: float | tf.Tensor) -> float | tf.Tensor:
  • """ convert signal to noise ratio (SNR) from linear to dB """
  • if tf.is_tensor(x):
  • return tf.math.log(x) * (10. / tf.math.log(10.))
  • else:
  • return math.log10(x) * 10.
  • def cast_to_int_if_eager(x: tf.Variable) -> int | tf.Variable:
  • return int(x) if tf.executing_eagerly() else x
  • ```
  • Are there best practices for such functions? Or maybe helpful predefined functions from tensorflow which make my life easier?
#2: Post edited by user avatar daniel_s‭ · 2023-09-07T10:32:01Z (over 1 year ago)
  • I regularly run into the problem that I have a python function which I want to use in both, eager and graph execution mode. I therefor have to adjust code so that it can handle both situations. Here are two examples:
  • `
  • import tensorflow as tf
  • def lin_to_db(x: float | tf.Tensor) -> float | tf.Tensor:
  • """ convert signal to noise ratio (SNR) from linear to dB """
  • if tf.is_tensor(x):
  • return tf.math.log(x) * (10. / tf.math.log(10.))
  • else:
  • return math.log10(x) * 10.
  • def cast_to_int_if_eager(x: tf.Variable) -> int | tf.Variable:
  • return int(x) if tf.executing_eagerly() else x
  • `
  • Are there best practices for such functions? Or maybe helpful predefined functions from tensorflow which make my life easier?
  • I regularly run into the problem that I have a python function which I want to use in both, eager and graph execution mode. I therefor have to adjust code so that it can handle both situations. Here are two examples:
  • import tensorflow as tf
  • def lin_to_db(x: float | tf.Tensor) -> float | tf.Tensor:
  • """ convert signal to noise ratio (SNR) from linear to dB """
  • if tf.is_tensor(x):
  • return tf.math.log(x) * (10. / tf.math.log(10.))
  • else:
  • return math.log10(x) * 10.
  • def cast_to_int_if_eager(x: tf.Variable) -> int | tf.Variable:
  • return int(x) if tf.executing_eagerly() else x
  • Are there best practices for such functions? Or maybe helpful predefined functions from tensorflow which make my life easier?
#1: Initial revision by user avatar daniel_s‭ · 2023-09-07T10:31:38Z (over 1 year ago)
Tensorflow: Best practices to write functions for both, modes, eager and graph
I regularly run into the problem that I have a python function which I want to use in both, eager and graph execution mode. I therefor have to adjust code so that it can handle both situations. Here are two examples:

`
    import tensorflow as tf

    def lin_to_db(x: float | tf.Tensor) -> float | tf.Tensor:
        """ convert signal to noise ratio (SNR) from linear to dB """

        if tf.is_tensor(x):
            return tf.math.log(x) * (10. / tf.math.log(10.))
        else:
            return math.log10(x) * 10.

    def cast_to_int_if_eager(x: tf.Variable) -> int | tf.Variable:
        return int(x) if tf.executing_eagerly() else x
`

Are there best practices for such functions? Or maybe helpful predefined functions from tensorflow which make my life easier?