tf.data.experimental.ignore_errors
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Creates a Dataset
from another Dataset
and silently ignores any errors.
tf.data.experimental.ignore_errors(
log_warning=False
)
Use this transformation to produce a dataset that contains the same elements
as the input, but silently drops any elements that caused an error. For
example:
dataset = tf.data.Dataset.from_tensor_slices([1., 2., 0., 4.])
# Computing `tf.debugging.check_numerics(1. / 0.)` will raise an
InvalidArgumentError.
dataset = dataset.map(lambda x: tf.debugging.check_numerics(1. / x, "error"))
# Using `ignore_errors()` will drop the element that causes an error.
dataset =
dataset.apply(tf.data.experimental.ignore_errors()) # ==> {1., 0.5, 0.2}
Args:
log_warning: (Optional.) A 'tf.bool' scalar indicating whether ignored
errors should be logged to stderr. Defaults to 'False'.
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Last updated 2021-02-18 UTC.
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