tf.compat.v1.data.FixedLengthRecordDataset

A Dataset of fixed-length records from one or more binary files.

Inherits From: Dataset, Dataset

tf.compat.v1.data.FixedLengthRecordDataset(
    filenames, record_bytes, header_bytes=None, footer_bytes=None, buffer_size=None,
    compression_type=None, num_parallel_reads=None
)

Args
`filenames`	A `tf.string` tensor or `tf.data.Dataset` containing one or more filenames.
`record_bytes`	A `tf.int64` scalar representing the number of bytes in each record.
`header_bytes`	(Optional.) A `tf.int64` scalar representing the number of bytes to skip at the start of a file.
`footer_bytes`	(Optional.) A `tf.int64` scalar representing the number of bytes to ignore at the end of a file.
`buffer_size`	(Optional.) A `tf.int64` scalar representing the number of bytes to buffer when reading.
`compression_type`	(Optional.) A `tf.string` scalar evaluating to one of `""` (no compression), `"ZLIB"`, or `"GZIP"`.
`num_parallel_reads`	(Optional.) A `tf.int64` scalar representing the number of files to read in parallel. If greater than one, the records of files read in parallel are outputted in an interleaved order. If your input pipeline is I/O bottlenecked, consider setting this parameter to a value greater than one to parallelize the I/O. If `None`, files will be read sequentially.

Attributes
`element_spec`	The type specification of an element of this dataset. `dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])` `dataset.element_spec` `TensorSpec(shape=(), dtype=tf.int32, name=None)` For more information, read this guide.
`output_classes`	Returns the class of each component of an element of this dataset. (deprecated) Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use `tf.compat.v1.data.get_output_classes(dataset)`.
`output_shapes`	Returns the shape of each component of an element of this dataset. (deprecated) Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use `tf.compat.v1.data.get_output_shapes(dataset)`.
`output_types`	Returns the type of each component of an element of this dataset. (deprecated) Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use `tf.compat.v1.data.get_output_types(dataset)`.

Methods

`apply`

View source

apply(
    transformation_func
)

Applies a transformation function to this dataset.

apply enables chaining of custom Dataset transformations, which are represented as functions that take one Dataset argument and return a transformed 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]

Args
`transformation_func`	A function that takes one `Dataset` argument and returns a `Dataset`.

Returns
`Dataset`	The `Dataset` returned by applying `transformation_func` to this dataset.

`as_numpy_iterator`

View source

as_numpy_iterator()

Returns an iterator which converts all elements of the dataset to numpy.

Use as_numpy_iterator to inspect the content of your dataset. To see element shapes and types, print dataset elements directly instead of using 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`

View source

batch(
    batch_size, drop_remainder=False, num_parallel_calls=None, deterministic=None
)

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.
`num_parallel_calls`	(Optional.) A `tf.int64` scalar `tf.Tensor`, representing the number of batches to compute asynchronously in parallel. If not specified, batches will be computed sequentially. If the value `tf.data.AUTOTUNE` is used, then the number of parallel calls is set dynamically based on available resources.
`deterministic`	(Optional.) When `num_parallel_calls` is specified, if this boolean is specified (`True` or `False`), it controls the order in which the transformation produces elements. If set to `False`, the transformation is allowed to yield elements out of order to trade determinism for performance. If not specified, the `tf.data.Options.experimental_deterministic` option (`True` by default) controls the behavior.

Returns
`Dataset`	A `Dataset`.

`cache`

View source

cache(
    filename=''
)

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

View source

cardinality()

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 (e.g. 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`

View source

concatenate(
    dataset
)

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
# compatible element specs.
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`

View source

enumerate(
    start=0
)

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`

View source

filter(
    predicate
)

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`.

`filter_with_legacy_function`

View source

filter_with_legacy_function(
    predicate
)

Filters this dataset according to predicate. (deprecated)

Args
`predicate`	A function mapping a (nested) structure of tensors (having shapes and types defined by `self.output_shapes` and `self.output_types`) to a scalar `tf.bool` tensor.

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

`flat_map`

View source

flat_map(
    map_func
)

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`

View source

@staticmethod
from_generator(
    generator, output_types=None, output_shapes=None, args=None,
    output_signature=None
)

Creates a Dataset whose elements are generated by generator. (deprecated arguments)

The generator argument must be a callable object that returns an object that supports the iter() protocol (e.g. a generator function).

The elements generated by generator must be compatible with either the given output_signature argument or with the given output_types and (optionally) output_shapes arguments, whichever was specified.

The recommended way to call from_generator is to use the output_signature argument. In this case the output will be assumed to consist of objects with the classes, shapes and types defined by tf.TypeSpec objects from output_signature argument:

def gen():
  ragged_tensor = tf.ragged.constant([[1, 2], [3]])
  yield 42, ragged_tensor

dataset = tf.data.Dataset.from_generator(
     gen,
     output_signature=(
         tf.TensorSpec(shape=(), dtype=tf.int32),
         tf.RaggedTensorSpec(shape=(2, None), dtype=tf.int32)))

list(dataset.take(1))
[(<tf.Tensor: shape=(), dtype=int32, numpy=42>,
<tf.RaggedTensor [[1, 2], [3]]>)]

There is also a deprecated way to call from_generator by either with output_types argument alone or together with output_shapes argument. In this case the output of the function will be assumed to consist of tf.Tensor objects with the types defined by output_types and with the shapes which are either unknown or defined by output_shapes.

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`	(Optional.) 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.
`output_signature`	(Optional.) A (nested) structure of `tf.TypeSpec` objects corresponding to each component of an element yielded by `generator`.

Returns
`Dataset`	A `Dataset`.

`from_sparse_tensor_slices`

View source

@staticmethod
from_sparse_tensor_slices(
    sparse_tensor
)

Splits each rank-N tf.sparse.SparseTensor in this dataset row-wise. (deprecated)

Args
`sparse_tensor`	A `tf.sparse.SparseTensor`.

Returns
`Dataset`	A `Dataset` of rank-(N-1) sparse tensors.

`from_tensor_slices`

View source

@staticmethod
from_tensor_slices(
    tensors
)

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, whose components have the same first dimension. Supported values are documented here.

Returns
`Dataset`	A `Dataset`.

`from_tensors`

View source

@staticmethod
from_tensors(
    tensors
)

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)]

Args
`tensors`	A dataset "element". Supported values are documented here.

Returns
`Dataset`	A `Dataset`.

`interleave`

View source

interleave(
    map_func, cycle_length=None, block_length=None, num_parallel_calls=None,
    deterministic=None
)

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.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.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.AUTOTUNE` is used, then the number of parallel calls is set dynamically based on available CPU.
`deterministic`	(Optional.) When `num_parallel_calls` is specified, if this boolean is specified (`True` or `False`), it controls the order in which the transformation produces elements. If set to `False`, the transformation is allowed to yield elements out of order to trade determinism for performance. If not specified, the `tf.data.Options.experimental_deterministic` option (`True` by default) controls the behavior.

Returns
`Dataset`	A `Dataset`.

`list_files`

View source

@staticmethod
list_files(
    file_pattern, shuffle=None, seed=None
)

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 (i.e. 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.

`make_initializable_iterator`

View source

make_initializable_iterator(
    shared_name=None
)

Creates an iterator for elements of this dataset. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: This is a deprecated API that should only be used in TF 1 graph mode and legacy TF 2 graph mode available through tf.compat.v1. In all other situations -- namely, eager mode and inside tf.function -- you can consume dataset elements using for elem in dataset: ... or by explicitly creating iterator via iterator = iter(dataset) and fetching its elements via values = next(iterator). Furthermore, this API is not available in TF 2. During the transition from TF 1 to TF 2 you can use tf.compat.v1.data.make_initializable_iterator(dataset) to create a TF 1 graph mode style iterator for a dataset created through TF 2 APIs. Note that this should be a transient state of your code base as there are in general no guarantees about the interoperability of TF 1 and TF 2 code.

# Building graph ...
dataset = ...
iterator = dataset.make_initializable_iterator()
next_value = iterator.get_next()  # This is a Tensor.

# ... from within a session ...
sess.run(iterator.initializer)
try:
  while True:
    value = sess.run(next_value)
    ...
except tf.errors.OutOfRangeError:
    pass

Args
`shared_name`	(Optional.) If non-empty, the returned iterator will be shared under the given name across multiple sessions that share the same devices (e.g. when using a remote server).

Returns
A `tf.data.Iterator` for elements of this dataset.

Raises
`RuntimeError`	If eager execution is enabled.

`make_one_shot_iterator`

View source

make_one_shot_iterator()

Creates an iterator for elements of this dataset. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: This is a deprecated API that should only be used in TF 1 graph mode and legacy TF 2 graph mode available through tf.compat.v1. In all other situations -- namely, eager mode and inside tf.function -- you can consume dataset elements using for elem in dataset: ... or by explicitly creating iterator via iterator = iter(dataset) and fetching its elements via values = next(iterator). Furthermore, this API is not available in TF 2. During the transition from TF 1 to TF 2 you can use tf.compat.v1.data.make_one_shot_iterator(dataset) to create a TF 1 graph mode style iterator for a dataset created through TF 2 APIs. Note that this should be a transient state of your code base as there are in general no guarantees about the interoperability of TF 1 and TF 2 code.

Example:

# Building graph ...
dataset = ...
next_value = dataset.make_one_shot_iterator().get_next()

# ... from within a session ...
try:
  while True:
    value = sess.run(next_value)
    ...
except tf.errors.OutOfRangeError:
    pass

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

`map`

View source

map(
    map_func, num_parallel_calls=None, deterministic=None
)

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. Supported structure constructs are documented here.

For example, map can be used for 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.AUTOTUNE,
    deterministic=False)

The order of elements yielded by this transformation is deterministic if deterministic=True. If map_func contains stateful operations and num_parallel_calls > 1, the order in which that state is accessed is undefined, so the values of output elements may not be deterministic regardless of the deterministic flag value.

Args
`map_func`	A function mapping a dataset element to another dataset element.
`num_parallel_calls`	(Optional.) A `tf.int64` 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.AUTOTUNE` is used, then the number of parallel calls is set dynamically based on available CPU.
`deterministic`	(Optional.) When `num_parallel_calls` is specified, if this boolean is specified (`True` or `False`), it controls the order in which the transformation produces elements. If set to `False`, the transformation is allowed to yield elements out of order to trade determinism for performance. If not specified, the `tf.data.Options.experimental_deterministic` option (`True` by default) controls the behavior.

Returns
`Dataset`	A `Dataset`.

`map_with_legacy_function`

View source

map_with_legacy_function(
    map_func, num_parallel_calls=None, deterministic=None
)

Maps map_func across the elements of this dataset. (deprecated)

Args
`map_func`	A function mapping a (nested) structure of tensors (having shapes and types defined by `self.output_shapes` and `self.output_types`) to another (nested) structure of tensors.
`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.AUTOTUNE` is used, then the number of parallel calls is set dynamically based on available CPU.
`deterministic`	(Optional.) When `num_parallel_calls` is specified, this boolean controls the order in which the transformation produces elements. If set to `False`, the transformation is allowed to yield elements out of order to trade determinism for performance. If not specified, the `tf.data.Options.experimental_deterministic` option (`True` by default) controls the behavior.

Returns
`Dataset`	A `Dataset`.

`options`

View source

options()

Returns the options for this dataset and its inputs.

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

`padded_batch`

View source

padded_batch(
    batch_size, padded_shapes=None, padding_values=None, drop_remainder=False
)

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 (nested) 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

prefetch(
    buffer_size
)

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. If the value `tf.data.AUTOTUNE` is used, then the buffer size is dynamically tuned.

Returns
`Dataset`	A `Dataset`.

`range`

View source

@staticmethod
range(
    *args, **kwargs
)

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].

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`).

**kwargs

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

Returns
`Dataset`	A `RangeDataset`.

Raises
`ValueError`	if len(args) == 0.

`reduce`

View source

reduce(
    initial_state, reduce_func
)

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

repeat(
    count=None
)

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

shard(
    num_shards, index
)

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. Note: error checking is done on a best-effort basis, and errors aren't guaranteed to be caught upon dataset creation. (e.g. providing in a placeholder tensor bypasses the early checking, and will instead result in an error during a session.run call.)

`shuffle`

View source

shuffle(
    buffer_size, seed=None, reshuffle_each_iteration=None
)

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 (i.e. 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)
# [1, 0, 2, 1, 2, 0]

dataset = tf.data.Dataset.range(3)
dataset = dataset.shuffle(3, reshuffle_each_iteration=False)
dataset = dataset.repeat(2)
# [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())
# [1, 0, 2]
list(dataset.as_numpy_iterator())
# [1, 2, 0]

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

skip(
    count
)

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

take(
    count
)

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

unbatch()

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

window(
    size, shift=None, stride=1, drop_remainder=False
)

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 windows should be dropped if their 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

with_options(
    options
)

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

@staticmethod
zip(
    datasets
)

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 a (nested) structure of Dataset objects. The supported nesting mechanisms are documented here.

# 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

__bool__()

`iter`

View source

__iter__()

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

__len__()

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

__nonzero__()

tf.compat.v1.data.FixedLengthRecordDataset

Args

Attributes

Methods

apply

as_numpy_iterator

batch

cache

cardinality

concatenate

enumerate

filter

filter_with_legacy_function

flat_map

from_generator

from_sparse_tensor_slices

from_tensor_slices

from_tensors

interleave

For example:

list_files

Example:

make_initializable_iterator

make_one_shot_iterator

Example:

map

map_with_legacy_function

options

padded_batch

prefetch

range

reduce

repeat

shard

Important caveats:

shuffle

skip

take

unbatch

window

For example:

with_options

zip

__bool__

__iter__

__len__

__nonzero__

`apply`

`as_numpy_iterator`

`batch`

`cache`

`cardinality`

`concatenate`

`enumerate`

`filter`

`filter_with_legacy_function`

`flat_map`

`from_generator`

`from_sparse_tensor_slices`

`from_tensor_slices`

`from_tensors`

`interleave`

`list_files`

`make_initializable_iterator`

`make_one_shot_iterator`

`map`

`map_with_legacy_function`

`options`

`padded_batch`

`prefetch`

`range`

`reduce`

`repeat`

`shard`

`shuffle`

`skip`

`take`

`unbatch`

`window`

`with_options`

`zip`

`bool`

`iter`

`len`

`nonzero`