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Tensorflow functions should typically work on both eager and graph tensors. This means that you can just use the following implementation: def lin_to_db(x: float | tf.Tensor) -> tf.Tensor: ...
Answer
#1: Initial revision
by
mr Tsjolder
·
2023-09-09T13:42:49Z (about 1 year ago)
Tensorflow functions should typically work on both eager and graph tensors.
This means that you can just use the following implementation:
```python
def lin_to_db(x: float | tf.Tensor) -> tf.Tensor:
""" convert signal to noise ratio (SNR) from linear to dB """
return 10. * tf.math.log(x) / tf.math.log(10.)
```
As you correctly pointed out, this does affect the output in the sense that the output will always be a `tf.Tensor`, even if the input is a `float`.
You seem to depict it as a disadvantage, but I would argue that this is actually an advantage.
After all, no matter what type the input is (`float`, `tf.Tensor`, `np.ndarray`, ...) the output will always have the same, known type.
If you need the resulting tensor to some other type, you can always convert it as follows:
```python
lin_to_db(x).numpy().item()
```
Note that this code works for any `x` that can be (implicitly) converted to a `tf.Tensor`.