tf.debugging.check_numerics
Stay organized with collections
Save and categorize content based on your preferences.
Checks a tensor for NaN and Inf values.
tf.debugging.check_numerics(
tensor: Annotated[Any, TV_CheckNumerics_T], message: str, name=None
) -> Annotated[Any, TV_CheckNumerics_T]
Used in the notebooks
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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[]]