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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

Question python tensorflow tensorflow-eager-mode tensorflow-graph-mode tensorflow-function

#5: Post edited by

daniel_s‭ · 2023-09-11T08:27:02Z (about 1 year ago)

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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

meta user‭ · 2023-09-09T18:18:53Z (about 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))

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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?

python tensorflow tensorflow-eager-mode tensorflow-graph-mode tensorflow-function

#3: Post edited by

Alexei‭ · 2023-09-08T16:33:51Z (about 1 year ago)
minor fixes + improved code formatting

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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

daniel_s‭ · 2023-09-07T10:32:01Z (about 1 year ago)

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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

daniel_s‭ · 2023-09-07T10:31:38Z (about 1 year ago)

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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?

python tensorflow tensorflow-eager-mode tensorflow-graph-mode tensorflow-function

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