triggered tf.function retracing warning

[lzyrapx]

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we only address code/doc bugs, performance issues, feature requests and
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System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 16.04
• TensorFlow installed from (source or binary): python3 pip
• TensorFlow version (use command below): 2.3.1
• Python version: python 3.6

You can collect some of this information using our environment capture
script
You can also obtain the TensorFlow version with:

1. TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"
2. TF 2.0: python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"

Describe the current behavior
WARNING:tensorflow: triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop,
(2) passing tensors with different shapes,
(3) passing Python objects instead of tensors.
For (1), please define your @tf.function outside of the loop.
For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing.
For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.

I add experimental_relax_shapes=True option in my code but not work.

Describe the expected behavior
I would like not to have retracing if there is no need.

Standalone code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.

Other info / logs Include any logs or source code that would be helpful to
diagnose the problem. If including tracebacks, please include the full
traceback. Large logs and files should be attached.

example code

import tensorflow as tf
from random import randint
from tensorflow.keras.layers import Conv1D
from tensorflow.keras.models import Sequential

model = Sequential()
model.add(Conv1D(8, 3))
model.build([None, 12, 1])

predict_tensors = [
    tf.random.normal([randint(1, 8), randint(4, 40), 1])
    for _ in range(10)
]
for t in predict_tensors:
    print(model.predict(t))

[amahendrakar]

@lzyrapx,
On running the code with TF v2.3, I did not face any warnings. Please find the gist of it here.

Could you please check if you are facing the same issue in a virtual environment?

Alternatively, you can also try suppressing the warnings by running the below code before importing TensorFlow.

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' 
import tensorflow as tf

Thanks!

[google-ml-butler]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you.

[google-ml-butler]

Closing as stale. Please reopen if you'd like to work on this further.