tf.data.experimental.ignore_errors

Creates a Dataset from another Dataset and silently ignores any errors. (deprecated)

tf.data.experimental.ignore_errors(
    log_warning=False
)

Used in the notebooks

Used in the tutorials

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'.

A Dataset transformation function, which can be passed to tf.data.Dataset.apply.