tf.debugging.check_numerics | TensorFlow v2.16.1 (original) (raw)
tf.debugging.check_numerics
Stay organized with collections Save and categorize content based on your preferences.
Checks a tensor for NaN and Inf values.
View aliases
Compat aliases for migration
SeeMigration guide for more details.
tf.debugging.check_numerics(
tensor: Annotated[Any, TV_CheckNumerics_T], message: str, name=None
) -> Annotated[Any, TV_CheckNumerics_T]
Used in the notebooks
| Used in the guide |
|---|
| TensorFlow graph optimization with Grappler |
When run, reports an InvalidArgument error if tensor has any values that are not a number (NaN) or infinity (Inf). Otherwise, returns the input tensor.
Example usage:
a = tf.Variable(1.0)
tf.debugging.check_numerics(a, message='')
b = tf.Variable(np.nan)
try:
tf.debugging.check_numerics(b, message='Checking b')
except Exception as e:
assert "Checking b : Tensor had NaN values" in e.message
c = tf.Variable(np.inf)
try:
tf.debugging.check_numerics(c, message='Checking c')
except Exception as e:
assert "Checking c : Tensor had Inf values" in e.message
| Args | |
|---|---|
| tensor | A Tensor. Must be one of the following types: bfloat16, half, float32, float64. |
| message | A string. Prefix of the error message. |
| name | A name for the operation (optional). |
| Returns |
|---|
| A Tensor. Has the same type as tensor. |
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
Last updated 2024-04-26 UTC.