tf.data.experimental.group_by

TensorFlow 1 version

View source on GitHub

Creates a Dataset from another Dataset and silently ignores any errors.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.data.experimental.ignore_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'.

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

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.data / experimental / experimental.group_by_reducer

View aliases

Returns