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tf.data.TFRecordDataset

참조 박을

TensorFlow 1 개 버전 GitHub의에서 소스보기

Dataset 하나 개 이상의 TFRecord 파일에서 레코드를 포함하는 것을 특징으로하는 방법.

: 상속 Dataset

전자 필기장에서 사용

가이드에서 사용 튜토리얼에서 사용

filenames tf.string 텐서 또는 tf.data.Dataset 하나 이상의 파일 이름을 포함.
compression_type (선택.)를 tf.string 중 하나로 평가 스칼라 "" (비 압축) "ZLIB" 또는 "GZIP" .
buffer_size (선택.)를 tf.int64 스칼라 읽기 버퍼의 바이트 수를 나타내는. 귀하의 의견 파이프 라인이 나는 경우 / O가 병목, 값 1-100 MB들을이 매개 변수를 설정하는 것이 좋습니다. 경우 None , 로컬 및 원격 파일 시스템을위한 합리적인 기본값이 사용됩니다.
num_parallel_reads (선택.)를 tf.int64 파일의 수를 나타내는 스칼라 병렬로 판독한다. 1보다 큰 파일의 기록을 병렬로 판독하는 경우, 인터리브 순서로 출력된다. 귀하의 의견 파이프 라인 인 경우의 I / O가 병목의 I / O를 병렬 처리하는 것보다는 값 이상으로이 매개 변수를 설정하는 것이 좋습니다. 경우 None , 파일을 순차적으로 읽을 수 있습니다.

TypeError 모든 인수는 예상 유형이없는 경우.
ValueError 모든 인수는 예상 모양이없는 경우.

element_spec 이 데이터 집합의 원소의 종류 사양.

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
dataset.element_spec
TensorSpec(shape=(), dtype=tf.int32, name=None)

행동 양식

apply

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이 데이터 세트를 변환 함수를 적용한다.

apply 사용자의 체인 가능 Dataset 중 하나 개 인 함수로 표현되는 변환을, Dataset 인수 및 반환 변환 Dataset .

dataset = tf.data.Dataset.range(100)
def dataset_fn(ds):
  return ds.filter(lambda x: x < 5)
dataset = dataset.apply(dataset_fn)
list(dataset.as_numpy_iterator())
[0, 1, 2, 3, 4]

인수
transformation_func 하나 개 걸리는 함수 Dataset 인수와 반환 Dataset .

보고
Dataset Dataset 에 적용하여 반환 transformation_func 이 데이터 집합에.

as_numpy_iterator

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NumPy와의 데이터 세트의 모든 요소를 ​​변환하는 반복자를 돌려줍니다.

사용 as_numpy_iterator 데이터 집합의 내용을 검사 할 수 있습니다. 요소의 모양과 유형을 참조를 사용하는 대신 직접 데이터 세트 요소를 인쇄하려면 as_numpy_iterator .

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
for element in dataset:
  print(element)
tf.Tensor(1, shape=(), dtype=int32)
tf.Tensor(2, shape=(), dtype=int32)
tf.Tensor(3, shape=(), dtype=int32)

This method requires that you are running in eager mode and the dataset's element_spec contains only TensorSpec components.

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
for element in dataset.as_numpy_iterator():
  print(element)
1
2
3
dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
print(list(dataset.as_numpy_iterator()))
[1, 2, 3]

as_numpy_iterator() will preserve the nested structure of dataset elements.

dataset = tf.data.Dataset.from_tensor_slices({'a': ([1, 2], [3, 4]),
                                              'b': [5, 6]})
list(dataset.as_numpy_iterator()) == [{'a': (1, 3), 'b': 5},
                                      {'a': (2, 4), 'b': 6}]
True

Returns
An iterable over the elements of the dataset, with their tensors converted to numpy arrays.

Raises
TypeError if an element contains a non- Tensor value.
RuntimeError if eager execution is not enabled.

batch

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Combines consecutive elements of this dataset into batches.

dataset = tf.data.Dataset.range(8)
dataset = dataset.batch(3)
list(dataset.as_numpy_iterator())
[array([0, 1, 2]), array([3, 4, 5]), array([6, 7])]
dataset = tf.data.Dataset.range(8)
dataset = dataset.batch(3, drop_remainder=True)
list(dataset.as_numpy_iterator())
[array([0, 1, 2]), array([3, 4, 5])]

The components of the resulting element will have an additional outer dimension, which will be batch_size (or N % batch_size for the last element if batch_size does not divide the number of input elements N evenly and drop_remainder is False ). If your program depends on the batches having the same outer dimension, you should set the drop_remainder argument to True to prevent the smaller batch from being produced.

Args
batch_size A tf.int64 scalar tf.Tensor , representing the number of consecutive elements of this dataset to combine in a single batch.
drop_remainder (Optional.) A tf.bool scalar tf.Tensor , representing whether the last batch should be dropped in the case it has fewer than batch_size elements; the default behavior is not to drop the smaller batch.

Returns
Dataset A Dataset .

cache

View source

Caches the elements in this dataset.

The first time the dataset is iterated over, its elements will be cached either in the specified file or in memory. Subsequent iterations will use the cached data.

dataset = tf.data.Dataset.range(5)
dataset = dataset.map(lambda x: x**2)
dataset = dataset.cache()
# The first time reading through the data will generate the data using
# `range` and `map`.
list(dataset.as_numpy_iterator())
[0, 1, 4, 9, 16]
# Subsequent iterations read from the cache.
list(dataset.as_numpy_iterator())
[0, 1, 4, 9, 16]

When caching to a file, the cached data will persist across runs. Even the first iteration through the data will read from the cache file. Changing the input pipeline before the call to .cache() will have no effect until the cache file is removed or the filename is changed.

dataset = tf.data.Dataset.range(5)
dataset = dataset.cache("/path/to/file")  # doctest: +SKIP
list(dataset.as_numpy_iterator())  # doctest: +SKIP
[0, 1, 2, 3, 4]
dataset = tf.data.Dataset.range(10)
dataset = dataset.cache("/path/to/file")  # Same file! # doctest: +SKIP
list(dataset.as_numpy_iterator())  # doctest: +SKIP
[0, 1, 2, 3, 4]

Args
filename A tf.string scalar tf.Tensor , representing the name of a directory on the filesystem to use for caching elements in this Dataset. If a filename is not provided, the dataset will be cached in memory.

Returns
Dataset A Dataset .

cardinality

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Returns the cardinality of the dataset, if known.

cardinality may return tf.data.INFINITE_CARDINALITY if the dataset contains an infinite number of elements or tf.data.UNKNOWN_CARDINALITY if the analysis fails to determine the number of elements in the dataset (eg when the dataset source is a file).

dataset = tf.data.Dataset.range(42)
print(dataset.cardinality().numpy())
42
dataset = dataset.repeat()
cardinality = dataset.cardinality()
print((cardinality == tf.data.INFINITE_CARDINALITY).numpy())
True
dataset = dataset.filter(lambda x: True)
cardinality = dataset.cardinality()
print((cardinality == tf.data.UNKNOWN_CARDINALITY).numpy())
True

Returns
A scalar tf.int64 Tensor representing the cardinality of the dataset. If the cardinality is infinite or unknown, cardinality returns the named constants tf.data.INFINITE_CARDINALITY and tf.data.UNKNOWN_CARDINALITY respectively.

concatenate

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Creates a Dataset by concatenating the given dataset with this dataset.

a = tf.data.Dataset.range(1, 4)  # ==> [ 1, 2, 3 ]
b = tf.data.Dataset.range(4, 8)  # ==> [ 4, 5, 6, 7 ]
ds = a.concatenate(b)
list(ds.as_numpy_iterator())
[1, 2, 3, 4, 5, 6, 7]
# The input dataset and dataset to be concatenated should have the same
# nested structures and output types.
c = tf.data.Dataset.zip((a, b))
a.concatenate(c)
Traceback (most recent call last):
TypeError: Two datasets to concatenate have different types
<dtype: 'int64'> and (tf.int64, tf.int64)
d = tf.data.Dataset.from_tensor_slices(["a", "b", "c"])
a.concatenate(d)
Traceback (most recent call last):
TypeError: Two datasets to concatenate have different types
<dtype: 'int64'> and <dtype: 'string'>

Args
dataset Dataset to be concatenated.

Returns
Dataset A Dataset .

enumerate

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Enumerates the elements of this dataset.

It is similar to python's enumerate .

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
dataset = dataset.enumerate(start=5)
for element in dataset.as_numpy_iterator():
  print(element)
(5, 1)
(6, 2)
(7, 3)
# The nested structure of the input dataset determines the structure of
# elements in the resulting dataset.
dataset = tf.data.Dataset.from_tensor_slices([(7, 8), (9, 10)])
dataset = dataset.enumerate()
for element in dataset.as_numpy_iterator():
  print(element)
(0, array([7, 8], dtype=int32))
(1, array([ 9, 10], dtype=int32))

Args
start A tf.int64 scalar tf.Tensor , representing the start value for enumeration.

Returns
Dataset A Dataset .

filter

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Filters this dataset according to predicate .

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
dataset = dataset.filter(lambda x: x < 3)
list(dataset.as_numpy_iterator())
[1, 2]
# `tf.math.equal(x, y)` is required for equality comparison
def filter_fn(x):
  return tf.math.equal(x, 1)
dataset = dataset.filter(filter_fn)
list(dataset.as_numpy_iterator())
[1]

Args
predicate A function mapping a dataset element to a boolean.

Returns
Dataset The Dataset containing the elements of this dataset for which predicate is True .

flat_map

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Maps map_func across this dataset and flattens the result.

Use flat_map if you want to make sure that the order of your dataset stays the same. For example, to flatten a dataset of batches into a dataset of their elements:

dataset = tf.data.Dataset.from_tensor_slices(
               [[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dataset = dataset.flat_map(lambda x: Dataset.from_tensor_slices(x))
list(dataset.as_numpy_iterator())
[1, 2, 3, 4, 5, 6, 7, 8, 9]

tf.data.Dataset.interleave() is a generalization of flat_map , since flat_map produces the same output as tf.data.Dataset.interleave(cycle_length=1)

Args
map_func A function mapping a dataset element to a dataset.

Returns
Dataset A Dataset .

from_generator

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Creates a Dataset whose elements are generated by generator .

The generator argument must be a callable object that returns an object that supports the iter() protocol (eg a generator function). The elements generated by generator must be compatible with the given output_types and (optional) output_shapes arguments.

import itertools

def gen():
  for i in itertools.count(1):
    yield (i, [1] * i)

dataset = tf.data.Dataset.from_generator(
     gen,
     (tf.int64, tf.int64),
     (tf.TensorShape([]), tf.TensorShape([None])))

list(dataset.take(3).as_numpy_iterator())
[(1, array([1])), (2, array([1, 1])), (3, array([1, 1, 1]))]

Args
generator A callable object that returns an object that supports the iter() protocol. If args is not specified, generator must take no arguments; otherwise it must take as many arguments as there are values in args .
output_types A nested structure of tf.DType objects corresponding to each component of an element yielded by generator .
output_shapes (Optional.) A nested structure of tf.TensorShape objects corresponding to each component of an element yielded by generator .
args (Optional.) A tuple of tf.Tensor objects that will be evaluated and passed to generator as NumPy-array arguments.

Returns
Dataset A Dataset .

from_tensor_slices

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Creates a Dataset whose elements are slices of the given tensors.

The given tensors are sliced along their first dimension. This operation preserves the structure of the input tensors, removing the first dimension of each tensor and using it as the dataset dimension. All input tensors must have the same size in their first dimensions.

# Slicing a 1D tensor produces scalar tensor elements.
dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
list(dataset.as_numpy_iterator())
[1, 2, 3]
# Slicing a 2D tensor produces 1D tensor elements.
dataset = tf.data.Dataset.from_tensor_slices([[1, 2], [3, 4]])
list(dataset.as_numpy_iterator())
[array([1, 2], dtype=int32), array([3, 4], dtype=int32)]
# Slicing a tuple of 1D tensors produces tuple elements containing
# scalar tensors.
dataset = tf.data.Dataset.from_tensor_slices(([1, 2], [3, 4], [5, 6]))
list(dataset.as_numpy_iterator())
[(1, 3, 5), (2, 4, 6)]
# Dictionary structure is also preserved.
dataset = tf.data.Dataset.from_tensor_slices({"a": [1, 2], "b": [3, 4]})
list(dataset.as_numpy_iterator()) == [{'a': 1, 'b': 3},
                                      {'a': 2, 'b': 4}]
True
# Two tensors can be combined into one Dataset object.
features = tf.constant([[1, 3], [2, 1], [3, 3]]) # ==> 3x2 tensor
labels = tf.constant(['A', 'B', 'A']) # ==> 3x1 tensor
dataset = Dataset.from_tensor_slices((features, labels))
# Both the features and the labels tensors can be converted
# to a Dataset object separately and combined after.
features_dataset = Dataset.from_tensor_slices(features)
labels_dataset = Dataset.from_tensor_slices(labels)
dataset = Dataset.zip((features_dataset, labels_dataset))
# A batched feature and label set can be converted to a Dataset
# in similar fashion.
batched_features = tf.constant([[[1, 3], [2, 3]],
                                [[2, 1], [1, 2]],
                                [[3, 3], [3, 2]]], shape=(3, 2, 2))
batched_labels = tf.constant([['A', 'A'],
                              ['B', 'B'],
                              ['A', 'B']], shape=(3, 2, 1))
dataset = Dataset.from_tensor_slices((batched_features, batched_labels))
for element in dataset.as_numpy_iterator():
  print(element)
(array([[1, 3],
       [2, 3]], dtype=int32), array([[b'A'],
       [b'A']], dtype=object))
(array([[2, 1],
       [1, 2]], dtype=int32), array([[b'B'],
       [b'B']], dtype=object))
(array([[3, 3],
       [3, 2]], dtype=int32), array([[b'A'],
       [b'B']], dtype=object))

Note that if tensors contains a NumPy array, and eager execution is not enabled, the values will be embedded in the graph as one or more tf.constant operations. For large datasets (> 1 GB), this can waste memory and run into byte limits of graph serialization. If tensors contains one or more large NumPy arrays, consider the alternative described in this guide .

Args
tensors A dataset element, with each component having the same size in the first dimension.

Returns
Dataset A Dataset .

from_tensors

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Creates a Dataset with a single element, comprising the given tensors.

from_tensors produces a dataset containing only a single element. To slice the input tensor into multiple elements, use from_tensor_slices instead.

dataset = tf.data.Dataset.from_tensors([1, 2, 3])
list(dataset.as_numpy_iterator())
[array([1, 2, 3], dtype=int32)]
dataset = tf.data.Dataset.from_tensors(([1, 2, 3], 'A'))
list(dataset.as_numpy_iterator())
[(array([1, 2, 3], dtype=int32), b'A')]
# You can use `from_tensors` to produce a dataset which repeats
# the same example many times.
example = tf.constant([1,2,3])
dataset = tf.data.Dataset.from_tensors(example).repeat(2)
list(dataset.as_numpy_iterator())
[array([1, 2, 3], dtype=int32), array([1, 2, 3], dtype=int32)]

Note that if tensors contains a NumPy array, and eager execution is not enabled, the values will be embedded in the graph as one or more tf.constant operations. For large datasets (> 1 GB), this can waste memory and run into byte limits of graph serialization. If tensors contains one or more large NumPy arrays, consider the alternative described in this guide .

Args
tensors A dataset element.

Returns
Dataset A Dataset .

interleave

View source

Maps map_func across this dataset, and interleaves the results.

For example, you can use Dataset.interleave() to process many input files concurrently:

# Preprocess 4 files concurrently, and interleave blocks of 16 records
# from each file.
filenames = ["/var/data/file1.txt", "/var/data/file2.txt",
             "/var/data/file3.txt", "/var/data/file4.txt"]
dataset = tf.data.Dataset.from_tensor_slices(filenames)
def parse_fn(filename):
  return tf.data.Dataset.range(10)
dataset = dataset.interleave(lambda x:
    tf.data.TextLineDataset(x).map(parse_fn, num_parallel_calls=1),
    cycle_length=4, block_length=16)

The cycle_length and block_length arguments control the order in which elements are produced. cycle_length controls the number of input elements that are processed concurrently. If you set cycle_length to 1, this transformation will handle one input element at a time, and will produce identical results to tf.data.Dataset.flat_map . In general, this transformation will apply map_func to cycle_length input elements, open iterators on the returned Dataset objects, and cycle through them producing block_length consecutive elements from each iterator, and consuming the next input element each time it reaches the end of an iterator.

For example:

dataset = Dataset.range(1, 6)  # ==> [ 1, 2, 3, 4, 5 ]
# NOTE: New lines indicate "block" boundaries.
dataset = dataset.interleave(
    lambda x: Dataset.from_tensors(x).repeat(6),
    cycle_length=2, block_length=4)
list(dataset.as_numpy_iterator())
[1, 1, 1, 1,
 2, 2, 2, 2,
 1, 1,
 2, 2,
 3, 3, 3, 3,
 4, 4, 4, 4,
 3, 3,
 4, 4,
 5, 5, 5, 5,
 5, 5]

Performance can often be improved by setting num_parallel_calls so that interleave will use multiple threads to fetch elements. If determinism isn't required, it can also improve performance to set deterministic=False .

filenames = ["/var/data/file1.txt", "/var/data/file2.txt",
             "/var/data/file3.txt", "/var/data/file4.txt"]
dataset = tf.data.Dataset.from_tensor_slices(filenames)
dataset = dataset.interleave(lambda x: tf.data.TFRecordDataset(x),
    cycle_length=4, num_parallel_calls=tf.data.experimental.AUTOTUNE,
    deterministic=False)

Args
map_func A function mapping a dataset element to a dataset.
cycle_length (Optional.) The number of input elements that will be processed concurrently. If not set, the tf.data runtime decides what it should be based on available CPU. If num_parallel_calls is set to tf.data.experimental.AUTOTUNE , the cycle_length argument identifies the maximum degree of parallelism.
block_length (Optional.) The number of consecutive elements to produce from each input element before cycling to another input element. If not set, defaults to 1.
num_parallel_calls (Optional.) If specified, the implementation creates a threadpool, which is used to fetch inputs from cycle elements asynchronously and in parallel. The default behavior is to fetch inputs from cycle elements synchronously with no parallelism. If the value tf.data.experimental.AUTOTUNE is used, then the number of parallel calls is set dynamically based on available CPU.
deterministic (Optional.) A boolean controlling whether determinism should be traded for performance by allowing elements to be produced out of order. If deterministic is None , the tf.data.Options.experimental_deterministic dataset option ( True by default) is used to decide whether to produce elements deterministically.

Returns
Dataset A Dataset .

list_files

View source

A dataset of all files matching one or more glob patterns.

The file_pattern argument should be a small number of glob patterns. If your filenames have already been globbed, use Dataset.from_tensor_slices(filenames) instead, as re-globbing every filename with list_files may result in poor performance with remote storage systems.

Example:

If we had the following files on our filesystem:

  • /path/to/dir/a.txt
  • /path/to/dir/b.py
  • /path/to/dir/c.py

If we pass "/path/to/dir/*.py" as the directory, the dataset would produce:

  • /path/to/dir/b.py
  • /path/to/dir/c.py

Args
file_pattern A string, a list of strings, or a tf.Tensor of string type (scalar or vector), representing the filename glob (ie shell wildcard) pattern(s) that will be matched.
shuffle (Optional.) If True , the file names will be shuffled randomly. Defaults to True .
seed (Optional.) A tf.int64 scalar tf.Tensor , representing the random seed that will be used to create the distribution. See tf.random.set_seed for behavior.

Returns
Dataset A Dataset of strings corresponding to file names.

map

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Maps map_func across the elements of this dataset.

This transformation applies map_func to each element of this dataset, and returns a new dataset containing the transformed elements, in the same order as they appeared in the input. map_func can be used to change both the values and the structure of a dataset's elements. For example, adding 1 to each element, or projecting a subset of element components.

dataset = Dataset.range(1, 6)  # ==> [ 1, 2, 3, 4, 5 ]
dataset = dataset.map(lambda x: x + 1)
list(dataset.as_numpy_iterator())
[2, 3, 4, 5, 6]

The input signature of map_func is determined by the structure of each element in this dataset.

dataset = Dataset.range(5)
# `map_func` takes a single argument of type `tf.Tensor` with the same
# shape and dtype.
result = dataset.map(lambda x: x + 1)
# Each element is a tuple containing two `tf.Tensor` objects.
elements = [(1, "foo"), (2, "bar"), (3, "baz")]
dataset = tf.data.Dataset.from_generator(
    lambda: elements, (tf.int32, tf.string))
# `map_func` takes two arguments of type `tf.Tensor`. This function
# projects out just the first component.
result = dataset.map(lambda x_int, y_str: x_int)
list(result.as_numpy_iterator())
[1, 2, 3]
# Each element is a dictionary mapping strings to `tf.Tensor` objects.
elements =  ([{"a": 1, "b": "foo"},
              {"a": 2, "b": "bar"},
              {"a": 3, "b": "baz"}])
dataset = tf.data.Dataset.from_generator(
    lambda: elements, {"a": tf.int32, "b": tf.string})
# `map_func` takes a single argument of type `dict` with the same keys
# as the elements.
result = dataset.map(lambda d: str(d["a"]) + d["b"])

The value or values returned by map_func determine the structure of each element in the returned dataset.

dataset = tf.data.Dataset.range(3)
# `map_func` returns two `tf.Tensor` objects.
def g(x):
  return tf.constant(37.0), tf.constant(["Foo", "Bar", "Baz"])
result = dataset.map(g)
result.element_spec
(TensorSpec(shape=(), dtype=tf.float32, name=None), TensorSpec(shape=(3,), dtype=tf.string, name=None))
# Python primitives, lists, and NumPy arrays are implicitly converted to
# `tf.Tensor`.
def h(x):
  return 37.0, ["Foo", "Bar"], np.array([1.0, 2.0], dtype=np.float64)
result = dataset.map(h)
result.element_spec
(TensorSpec(shape=(), dtype=tf.float32, name=None), TensorSpec(shape=(2,), dtype=tf.string, name=None), TensorSpec(shape=(2,), dtype=tf.float64, name=None))
# `map_func` can return nested structures.
def i(x):
  return (37.0, [42, 16]), "foo"
result = dataset.map(i)
result.element_spec
((TensorSpec(shape=(), dtype=tf.float32, name=None),
  TensorSpec(shape=(2,), dtype=tf.int32, name=None)),
 TensorSpec(shape=(), dtype=tf.string, name=None))

map_func can accept as arguments and return any type of dataset element.

Note that irrespective of the context in which map_func is defined (eager vs. graph), tf.data traces the function and executes it as a graph. To use Python code inside of the function you have a few options:

1) Rely on AutoGraph to convert Python code into an equivalent graph computation. The downside of this approach is that AutoGraph can convert some but not all Python code.

2) Use tf.py_function , which allows you to write arbitrary Python code but will generally result in worse performance than 1). For example:

d = tf.data.Dataset.from_tensor_slices(['hello', 'world'])
# transform a string tensor to upper case string using a Python function
def upper_case_fn(t: tf.Tensor):
  return t.numpy().decode('utf-8').upper()
d = d.map(lambda x: tf.py_function(func=upper_case_fn,
          inp=[x], Tout=tf.string))
list(d.as_numpy_iterator())
[b'HELLO', b'WORLD']

3) Use tf.numpy_function , which also allows you to write arbitrary Python code. Note that tf.py_function accepts tf.Tensor whereas tf.numpy_function accepts numpy arrays and returns only numpy arrays. For example:

d = tf.data.Dataset.from_tensor_slices(['hello', 'world'])
def upper_case_fn(t: np.ndarray):
  return t.decode('utf-8').upper()
d = d.map(lambda x: tf.numpy_function(func=upper_case_fn,
          inp=[x], Tout=tf.string))
list(d.as_numpy_iterator())
[b'HELLO', b'WORLD']

Note that the use of tf.numpy_function and tf.py_function in general precludes the possibility of executing user-defined transformations in parallel (because of Python GIL).

Performance can often be improved by setting num_parallel_calls so that map will use multiple threads to process elements. If deterministic order isn't required, it can also improve performance to set deterministic=False .

dataset = Dataset.range(1, 6)  # ==> [ 1, 2, 3, 4, 5 ]
dataset = dataset.map(lambda x: x + 1,
    num_parallel_calls=tf.data.experimental.AUTOTUNE,
    deterministic=False)

Args
map_func A function mapping a dataset element to another dataset element.
num_parallel_calls (Optional.) A tf.int32 scalar tf.Tensor , representing the number elements to process asynchronously in parallel. If not specified, elements will be processed sequentially. If the value tf.data.experimental.AUTOTUNE is used, then the number of parallel calls is set dynamically based on available CPU.
deterministic (Optional.) A boolean controlling whether determinism should be traded for performance by allowing elements to be produced out of order. If deterministic is None , the tf.data.Options.experimental_deterministic dataset option ( True by default) is used to decide whether to produce elements deterministically.

Returns
Dataset A Dataset .

options

View source

Returns the options for this dataset and its inputs.

Returns
A tf.data.Options object representing the dataset options.

padded_batch

View source

Combines consecutive elements of this dataset into padded batches.

This transformation combines multiple consecutive elements of the input dataset into a single element.

Like tf.data.Dataset.batch , the components of the resulting element will have an additional outer dimension, which will be batch_size (or N % batch_size for the last element if batch_size does not divide the number of input elements N evenly and drop_remainder is False ). If your program depends on the batches having the same outer dimension, you should set the drop_remainder argument to True to prevent the smaller batch from being produced.

Unlike tf.data.Dataset.batch , the input elements to be batched may have different shapes, and this transformation will pad each component to the respective shape in padded_shapes . The padded_shapes argument determines the resulting shape for each dimension of each component in an output element:

  • If the dimension is a constant, the component will be padded out to that length in that dimension.
  • If the dimension is unknown, the component will be padded out to the maximum length of all elements in that dimension.
A = (tf.data.Dataset
     .range(1, 5, output_type=tf.int32)
     .map(lambda x: tf.fill([x], x)))
# Pad to the smallest per-batch size that fits all elements.
B = A.padded_batch(2)
for element in B.as_numpy_iterator():
  print(element)
[[1 0]
 [2 2]]
[[3 3 3 0]
 [4 4 4 4]]
# Pad to a fixed size.
C = A.padded_batch(2, padded_shapes=5)
for element in C.as_numpy_iterator():
  print(element)
[[1 0 0 0 0]
 [2 2 0 0 0]]
[[3 3 3 0 0]
 [4 4 4 4 0]]
# Pad with a custom value.
D = A.padded_batch(2, padded_shapes=5, padding_values=-1)
for element in D.as_numpy_iterator():
  print(element)
[[ 1 -1 -1 -1 -1]
 [ 2  2 -1 -1 -1]]
[[ 3  3  3 -1 -1]
 [ 4  4  4  4 -1]]
# Components of nested elements can be padded independently.
elements = [([1, 2, 3], [10]),
            ([4, 5], [11, 12])]
dataset = tf.data.Dataset.from_generator(
    lambda: iter(elements), (tf.int32, tf.int32))
# Pad the first component of the tuple to length 4, and the second
# component to the smallest size that fits.
dataset = dataset.padded_batch(2,
    padded_shapes=([4], [None]),
    padding_values=(-1, 100))
list(dataset.as_numpy_iterator())
[(array([[ 1,  2,  3, -1], [ 4,  5, -1, -1]], dtype=int32),
  array([[ 10, 100], [ 11,  12]], dtype=int32))]
# Pad with a single value and multiple components.
E = tf.data.Dataset.zip((A, A)).padded_batch(2, padding_values=-1)
for element in E.as_numpy_iterator():
  print(element)
(array([[ 1, -1],
       [ 2,  2]], dtype=int32), array([[ 1, -1],
       [ 2,  2]], dtype=int32))
(array([[ 3,  3,  3, -1],
       [ 4,  4,  4,  4]], dtype=int32), array([[ 3,  3,  3, -1],
       [ 4,  4,  4,  4]], dtype=int32))

See also tf.data.experimental.dense_to_sparse_batch , which combines elements that may have different shapes into a tf.sparse.SparseTensor .

Args
batch_size A tf.int64 scalar tf.Tensor , representing the number of consecutive elements of this dataset to combine in a single batch.
padded_shapes (Optional.) A nested structure of tf.TensorShape or tf.int64 vector tensor-like objects representing the shape to which the respective component of each input element should be padded prior to batching. Any unknown dimensions will be padded to the maximum size of that dimension in each batch. If unset, all dimensions of all components are padded to the maximum size in the batch. padded_shapes must be set if any component has an unknown rank.
padding_values (Optional.) A nested structure of scalar-shaped tf.Tensor , representing the padding values to use for the respective components. None represents that the nested structure should be padded with default values. Defaults are 0 for numeric types and the empty string for string types. The padding_values should have the same structure as the input dataset. If padding_values is a single element and the input dataset has multiple components, then the same padding_values will be used to pad every component of the dataset. If padding_values is a scalar, then its value will be broadcasted to match the shape of each component.
drop_remainder (Optional.) A tf.bool scalar tf.Tensor , representing whether the last batch should be dropped in the case it has fewer than batch_size elements; the default behavior is not to drop the smaller batch.

Returns
Dataset A Dataset .

Raises
ValueError If a component has an unknown rank, and the padded_shapes argument is not set.

prefetch

View source

Creates a Dataset that prefetches elements from this dataset.

Most dataset input pipelines should end with a call to prefetch . This allows later elements to be prepared while the current element is being processed. This often improves latency and throughput, at the cost of using additional memory to store prefetched elements.

dataset = tf.data.Dataset.range(3)
dataset = dataset.prefetch(2)
list(dataset.as_numpy_iterator())
[0, 1, 2]

Args
buffer_size A tf.int64 scalar tf.Tensor , representing the maximum number of elements that will be buffered when prefetching.

Returns
Dataset A Dataset .

range

View source

Creates a Dataset of a step-separated range of values.

list(Dataset.range(5).as_numpy_iterator())
[0, 1, 2, 3, 4]
list(Dataset.range(2, 5).as_numpy_iterator())
[2, 3, 4]
list(Dataset.range(1, 5, 2).as_numpy_iterator())
[1, 3]
list(Dataset.range(1, 5, -2).as_numpy_iterator())
[]
list(Dataset.range(5, 1).as_numpy_iterator())
[]
list(Dataset.range(5, 1, -2).as_numpy_iterator())
[5, 3]
list(Dataset.range(2, 5, output_type=tf.int32).as_numpy_iterator())
[2, 3, 4]
list(Dataset.range(1, 5, 2, output_type=tf.float32).as_numpy_iterator())
[1.0, 3.0]

Args
*args follows the same semantics as python's xrange. len(args) == 1 -> start = 0, stop = args[0], step = 1. len(args) == 2 -> start = args[0], stop = args[1], step = 1. len(args) == 3 -> start = args[0], stop = args[1], step = args[2].
**kwargs

  • output_type: Its expected dtype. (Optional, default: tf.int64 ).

Returns
Dataset A RangeDataset .

Raises
ValueError if len(args) == 0.

reduce

View source

Reduces the input dataset to a single element.

The transformation calls reduce_func successively on every element of the input dataset until the dataset is exhausted, aggregating information in its internal state. The initial_state argument is used for the initial state and the final state is returned as the result.

tf.data.Dataset.range(5).reduce(np.int64(0), lambda x, _: x + 1).numpy()
5
tf.data.Dataset.range(5).reduce(np.int64(0), lambda x, y: x + y).numpy()
10

Args
initial_state An element representing the initial state of the transformation.
reduce_func A function that maps (old_state, input_element) to new_state . It must take two arguments and return a new element The structure of new_state must match the structure of initial_state .

Returns
A dataset element corresponding to the final state of the transformation.

repeat

View source

Repeats this dataset so each original value is seen count times.

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
dataset = dataset.repeat(3)
list(dataset.as_numpy_iterator())
[1, 2, 3, 1, 2, 3, 1, 2, 3]

Args
count (Optional.) A tf.int64 scalar tf.Tensor , representing the number of times the dataset should be repeated. The default behavior (if count is None or -1 ) is for the dataset be repeated indefinitely.

Returns
Dataset A Dataset .

shard

View source

Creates a Dataset that includes only 1/ num_shards of this dataset.

shard is deterministic. The Dataset produced by A.shard(n, i) will contain all elements of A whose index mod n = i.

A = tf.data.Dataset.range(10)
B = A.shard(num_shards=3, index=0)
list(B.as_numpy_iterator())
[0, 3, 6, 9]
C = A.shard(num_shards=3, index=1)
list(C.as_numpy_iterator())
[1, 4, 7]
D = A.shard(num_shards=3, index=2)
list(D.as_numpy_iterator())
[2, 5, 8]

This dataset operator is very useful when running distributed training, as it allows each worker to read a unique subset.

When reading a single input file, you can shard elements as follows:

 d = tf.data.TFRecordDataset(input_file)
d = d.shard(num_workers, worker_index)
d = d.repeat(num_epochs)
d = d.shuffle(shuffle_buffer_size)
d = d.map(parser_fn, num_parallel_calls=num_map_threads)
 

Important caveats:

  • Be sure to shard before you use any randomizing operator (such as shuffle).
  • Generally it is best if the shard operator is used early in the dataset pipeline. For example, when reading from a set of TFRecord files, shard before converting the dataset to input samples. This avoids reading every file on every worker. The following is an example of an efficient sharding strategy within a complete pipeline:
 d = Dataset.list_files(pattern)
d = d.shard(num_workers, worker_index)
d = d.repeat(num_epochs)
d = d.shuffle(shuffle_buffer_size)
d = d.interleave(tf.data.TFRecordDataset,
                 cycle_length=num_readers, block_length=1)
d = d.map(parser_fn, num_parallel_calls=num_map_threads)
 

Args
num_shards A tf.int64 scalar tf.Tensor , representing the number of shards operating in parallel.
index A tf.int64 scalar tf.Tensor , representing the worker index.

Returns
Dataset A Dataset .

Raises
InvalidArgumentError if num_shards or index are illegal values.

shuffle

View source

Randomly shuffles the elements of this dataset.

This dataset fills a buffer with buffer_size elements, then randomly samples elements from this buffer, replacing the selected elements with new elements. For perfect shuffling, a buffer size greater than or equal to the full size of the dataset is required.

For instance, if your dataset contains 10,000 elements but buffer_size is set to 1,000, then shuffle will initially select a random element from only the first 1,000 elements in the buffer. Once an element is selected, its space in the buffer is replaced by the next (ie 1,001-st) element, maintaining the 1,000 element buffer.

reshuffle_each_iteration controls whether the shuffle order should be different for each epoch. In TF 1.X, the idiomatic way to create epochs was through the repeat transformation:

dataset = tf.data.Dataset.range(3)
dataset = dataset.shuffle(3, reshuffle_each_iteration=True)
dataset = dataset.repeat(2)  # doctest: +SKIP
[1, 0, 2, 1, 2, 0]
dataset = tf.data.Dataset.range(3)
dataset = dataset.shuffle(3, reshuffle_each_iteration=False)
dataset = dataset.repeat(2)  # doctest: +SKIP
[1, 0, 2, 1, 0, 2]

In TF 2.0, tf.data.Dataset objects are Python iterables which makes it possible to also create epochs through Python iteration:

dataset = tf.data.Dataset.range(3)
dataset = dataset.shuffle(3, reshuffle_each_iteration=True)
list(dataset.as_numpy_iterator())  # doctest: +SKIP
[1, 0, 2]
list(dataset.as_numpy_iterator())  # doctest: +SKIP
[1, 2, 0]
dataset = tf.data.Dataset.range(3)
dataset = dataset.shuffle(3, reshuffle_each_iteration=False)
list(dataset.as_numpy_iterator())  # doctest: +SKIP
[1, 0, 2]
list(dataset.as_numpy_iterator())  # doctest: +SKIP
[1, 0, 2]

Args
buffer_size A tf.int64 scalar tf.Tensor , representing the number of elements from this dataset from which the new dataset will sample.
seed (Optional.) A tf.int64 scalar tf.Tensor , representing the random seed that will be used to create the distribution. See tf.random.set_seed for behavior.
reshuffle_each_iteration (Optional.) A boolean, which if true indicates that the dataset should be pseudorandomly reshuffled each time it is iterated over. (Defaults to True .)

Returns
Dataset A Dataset .

skip

View source

Creates a Dataset that skips count elements from this dataset.

dataset = tf.data.Dataset.range(10)
dataset = dataset.skip(7)
list(dataset.as_numpy_iterator())
[7, 8, 9]

Args
count A tf.int64 scalar tf.Tensor , representing the number of elements of this dataset that should be skipped to form the new dataset. If count is greater than the size of this dataset, the new dataset will contain no elements. If count is -1, skips the entire dataset.

Returns
Dataset A Dataset .

take

View source

Creates a Dataset with at most count elements from this dataset.

dataset = tf.data.Dataset.range(10)
dataset = dataset.take(3)
list(dataset.as_numpy_iterator())
[0, 1, 2]

Args
count A tf.int64 scalar tf.Tensor , representing the number of elements of this dataset that should be taken to form the new dataset. If count is -1, or if count is greater than the size of this dataset, the new dataset will contain all elements of this dataset.

Returns
Dataset A Dataset .

unbatch

View source

Splits elements of a dataset into multiple elements.

For example, if elements of the dataset are shaped [B, a0, a1, ...] , where B may vary for each input element, then for each element in the dataset, the unbatched dataset will contain B consecutive elements of shape [a0, a1, ...] .

elements = [ [1, 2, 3], [1, 2], [1, 2, 3, 4] ]
dataset = tf.data.Dataset.from_generator(lambda: elements, tf.int64)
dataset = dataset.unbatch()
list(dataset.as_numpy_iterator())
[1, 2, 3, 1, 2, 1, 2, 3, 4]

Returns
A Dataset .

window

View source

Combines (nests of) input elements into a dataset of (nests of) windows.

A "window" is a finite dataset of flat elements of size size (or possibly fewer if there are not enough input elements to fill the window and drop_remainder evaluates to False ).

The shift argument determines the number of input elements by which the window moves on each iteration. If windows and elements are both numbered starting at 0, the first element in window k will be element k * shift of the input dataset. In particular, the first element of the first window will always be the first element of the input dataset.

The stride argument determines the stride of the input elements, and the shift argument determines the shift of the window.

For example:

dataset = tf.data.Dataset.range(7).window(2)
for window in dataset:
  print(list(window.as_numpy_iterator()))
[0, 1]
[2, 3]
[4, 5]
[6]
dataset = tf.data.Dataset.range(7).window(3, 2, 1, True)
for window in dataset:
  print(list(window.as_numpy_iterator()))
[0, 1, 2]
[2, 3, 4]
[4, 5, 6]
dataset = tf.data.Dataset.range(7).window(3, 1, 2, True)
for window in dataset:
  print(list(window.as_numpy_iterator()))
[0, 2, 4]
[1, 3, 5]
[2, 4, 6]

Note that when the window transformation is applied to a dataset of nested elements, it produces a dataset of nested windows.

nested = ([1, 2, 3, 4], [5, 6, 7, 8])
dataset = tf.data.Dataset.from_tensor_slices(nested).window(2)
for window in dataset:
  def to_numpy(ds):
    return list(ds.as_numpy_iterator())
  print(tuple(to_numpy(component) for component in window))
([1, 2], [5, 6])
([3, 4], [7, 8])
dataset = tf.data.Dataset.from_tensor_slices({'a': [1, 2, 3, 4]})
dataset = dataset.window(2)
for window in dataset:
  def to_numpy(ds):
    return list(ds.as_numpy_iterator())
  print({'a': to_numpy(window['a'])})
{'a': [1, 2]}
{'a': [3, 4]}

Args
size A tf.int64 scalar tf.Tensor , representing the number of elements of the input dataset to combine into a window. Must be positive.
shift (Optional.) A tf.int64 scalar tf.Tensor , representing the number of input elements by which the window moves in each iteration. Defaults to size . Must be positive.
stride (Optional.) A tf.int64 scalar tf.Tensor , representing the stride of the input elements in the sliding window. Must be positive. The default value of 1 means "retain every input element".
drop_remainder (Optional.) A tf.bool scalar tf.Tensor , representing whether the last window should be dropped if its size is smaller than size .

Returns
Dataset A Dataset of (nests of) windows -- a finite datasets of flat elements created from the (nests of) input elements.

with_options

View source

Returns a new tf.data.Dataset with the given options set.

The options are "global" in the sense they apply to the entire dataset. If options are set multiple times, they are merged as long as different options do not use different non-default values.

ds = tf.data.Dataset.range(5)
ds = ds.interleave(lambda x: tf.data.Dataset.range(5),
                   cycle_length=3,
                   num_parallel_calls=3)
options = tf.data.Options()
# This will make the interleave order non-deterministic.
options.experimental_deterministic = False
ds = ds.with_options(options)

Args
options A tf.data.Options that identifies the options the use.

Returns
Dataset A Dataset with the given options.

Raises
ValueError when an option is set more than once to a non-default value

zip

View source

Creates a Dataset by zipping together the given datasets.

This method has similar semantics to the built-in zip() function in Python, with the main difference being that the datasets argument can be an arbitrary nested structure of Dataset objects.

# The nested structure of the `datasets` argument determines the
# structure of elements in the resulting dataset.
a = tf.data.Dataset.range(1, 4)  # ==> [ 1, 2, 3 ]
b = tf.data.Dataset.range(4, 7)  # ==> [ 4, 5, 6 ]
ds = tf.data.Dataset.zip((a, b))
list(ds.as_numpy_iterator())
[(1, 4), (2, 5), (3, 6)]
ds = tf.data.Dataset.zip((b, a))
list(ds.as_numpy_iterator())
[(4, 1), (5, 2), (6, 3)]

# The `datasets` argument may contain an arbitrary number of datasets.
c = tf.data.Dataset.range(7, 13).batch(2)  # ==> [ [7, 8],
                                           #       [9, 10],
                                           #       [11, 12] ]
ds = tf.data.Dataset.zip((a, b, c))
for element in ds.as_numpy_iterator():
  print(element)
(1, 4, array([7, 8]))
(2, 5, array([ 9, 10]))
(3, 6, array([11, 12]))

# The number of elements in the resulting dataset is the same as
# the size of the smallest dataset in `datasets`.
d = tf.data.Dataset.range(13, 15)  # ==> [ 13, 14 ]
ds = tf.data.Dataset.zip((a, d))
list(ds.as_numpy_iterator())
[(1, 13), (2, 14)]

Args
datasets A nested structure of datasets.

Returns
Dataset A Dataset .

__bool__

View source

__iter__

View source

Creates an iterator for elements of this dataset.

The returned iterator implements the Python Iterator protocol.

Returns
An tf.data.Iterator for the elements of this dataset.

Raises
RuntimeError If not inside of tf.function and not executing eagerly.

__len__

View source

Returns the length of the dataset if it is known and finite.

This method requires that you are running in eager mode, and that the length of the dataset is known and non-infinite. When the length may be unknown or infinite, or if you are running in graph mode, use tf.data.Dataset.cardinality instead.

Returns
An integer representing the length of the dataset.

Raises
RuntimeError If the dataset length is unknown or infinite, or if eager execution is not enabled.

__nonzero__

View source