tf.data.experimental.RandomDataset

Class `RandomDataset`

A Dataset of pseudorandom values.

Aliases:

Class tf.compat.v1.data.experimental.RandomDataset
Class tf.data.experimental.RandomDataset

Defined in python/data/experimental/ops/random_ops.py.

`init`

__init__(seed=None)

A Dataset of pseudorandom values.

Properties

`output_classes`

Returns the class of each component of an element of this dataset. (deprecated)

The expected values are tf.Tensor and tf.SparseTensor.

Returns:

A nested structure of Python type objects corresponding to each component of an element of this dataset.

`output_shapes`

Returns the shape of each component of an element of this dataset. (deprecated)

Returns:

A nested structure of tf.TensorShape objects corresponding to each component of an element of this dataset.

`output_types`

Returns the type of each component of an element of this dataset. (deprecated)

Returns:

A nested structure of tf.DType objects corresponding to each component of an element of this dataset.

Methods

`iter`

__iter__()

`apply`

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.

For example:

dataset = (dataset.map(lambda x: x ** 2)
           .apply(group_by_window(key_func, reduce_func, window_size))
           .map(lambda x: x ** 3))

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.

`batch`

batch(
    batch_size,
    drop_remainder=False
)

Combines consecutive elements of this dataset into batches.

The tensors in 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`

cache(filename='')

Caches the elements in this dataset.

Args:

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

Returns:

Dataset: A Dataset.

`concatenate`

concatenate(dataset)

Creates a Dataset by concatenating given dataset with this dataset.

a = Dataset.range(1, 4)  # ==> [ 1, 2, 3 ]
b = Dataset.range(4, 8)  # ==> [ 4, 5, 6, 7 ]

# Input dataset and dataset to be concatenated should have same
# nested structures and output types.
# c = Dataset.range(8, 14).batch(2)  # ==> [ [8, 9], [10, 11], [12, 13] ]
# d = Dataset.from_tensor_slices([14.0, 15.0, 16.0])
# a.concatenate(c) and a.concatenate(d) would result in error.

a.concatenate(b)  # ==> [ 1, 2, 3, 4, 5, 6, 7 ]

Args:

dataset: Dataset to be concatenated.

Returns:

Dataset: A Dataset.

`enumerate`

enumerate(start=0)

Enumerates the elements of this dataset.

It is similar to python's enumerate.

For example:

# NOTE: The following examples use `{ ... }` to represent the
# contents of a dataset.
a = { 1, 2, 3 }
b = { (7, 8), (9, 10) }

# The nested structure of the `datasets` argument determines the
# structure of elements in the resulting dataset.
a.enumerate(start=5)) == { (5, 1), (6, 2), (7, 3) }
b.enumerate() == { (0, (7, 8)), (1, (9, 10)) }

Args:

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

Returns:

Dataset: A Dataset.

`filter`

filter(predicate)

Filters this dataset according to predicate.

d = tf.data.Dataset.from_tensor_slices([1, 2, 3])

d = d.filter(lambda x: x < 3)  # ==> [1, 2]

# `tf.math.equal(x, y)` is required for equality comparison
def filter_fn(x):
  return tf.math.equal(x, 1)

d = d.filter(filter_fn)  # ==> [1]

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.

`filter_with_legacy_function`

filter_with_legacy_function(predicate)

Filters this dataset according to predicate. (deprecated)

NOTE: This is an escape hatch for existing uses of filter that do not work with V2 functions. New uses are strongly discouraged and existing uses should migrate to filter as this method will be removed in V2.

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`

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:

a = Dataset.from_tensor_slices([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ])

a.flat_map(lambda x: Dataset.from_tensor_slices(x + 1)) # ==>
#  [ 2, 3, 4, 5, 6, 7, 8, 9, 10 ]

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 nested structure of tensors (having shapes and types defined by self.output_shapes and self.output_types) to a Dataset.

Returns:

Dataset: A Dataset.

`from_generator`

from_generator(
    generator,
    output_types,
    output_shapes=None,
    args=None
)

Creates a Dataset whose elements are generated by generator.

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

For example:

import itertools
tf.compat.v1.enable_eager_execution()

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

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

for value in ds.take(2):
  print value
# (1, array([1]))
# (2, array([1, 1]))

NOTE: The current implementation of Dataset.from_generator() uses tf.compat.v1.py_func and inherits the same constraints. In particular, it requires the Dataset- and Iterator-related operations to be placed on a device in the same process as the Python program that called Dataset.from_generator(). The body of generator will not be serialized in a GraphDef, and you should not use this method if you need to serialize your model and restore it in a different environment.

NOTE: If generator depends on mutable global variables or other external state, be aware that the runtime may invoke generator multiple times (in order to support repeating the Dataset) and at any time between the call to Dataset.from_generator() and the production of the first element from the generator. Mutating global variables or external state can cause undefined behavior, and we recommend that you explicitly cache any external state in generator before calling Dataset.from_generator().

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_sparse_tensor_slices`

from_sparse_tensor_slices(sparse_tensor)

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

Args:

sparse_tensor: A tf.SparseTensor.

Returns:

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

`from_tensor_slices`

from_tensor_slices(tensors)

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

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 nested structure of tensors, each having the same size in the 0th dimension.

Returns:

Dataset: A Dataset.

`from_tensors`

from_tensors(tensors)

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

Args:

tensors: A nested structure of tensors.

Returns:

Dataset: A Dataset.

`interleave`

interleave(
    map_func,
    cycle_length,
    block_length=1,
    num_parallel_calls=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", ...]
dataset = (Dataset.from_tensor_slices(filenames)
           .interleave(lambda x:
               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:

a = Dataset.range(1, 6)  # ==> [ 1, 2, 3, 4, 5 ]

# NOTE: New lines indicate "block" boundaries.
a.interleave(lambda x: Dataset.from_tensors(x).repeat(6),
            cycle_length=2, block_length=4)  # ==> [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]

NOTE: The order of elements yielded by this transformation is deterministic, as long as map_func is a pure function. If map_func contains any stateful operations, the order in which that state is accessed is undefined.

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 a Dataset.
cycle_length: The number of elements from this dataset that will be processed concurrently.
block_length: The number of consecutive elements to produce from each input element before cycling to another input element.
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.

Returns:

Dataset: A Dataset.

`list_files`

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

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

NOTE: The default behavior of this method is to return filenames in a non-deterministic random shuffled order. Pass a seed or shuffle=False to get results in a deterministic order.

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.compat.v1.set_random_seed for behavior.

Returns:

Dataset: A Dataset of strings corresponding to file names.

`make_initializable_iterator`

make_initializable_iterator(shared_name=None)

Creates an Iterator for enumerating the elements of this dataset. (deprecated)

dataset = ...
iterator = dataset.make_initializable_iterator()
# ...
sess.run(iterator.initializer)

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:

An Iterator over the elements of this dataset.

Raises:

RuntimeError: If eager execution is enabled.

`make_one_shot_iterator`

make_one_shot_iterator()

Creates an Iterator for enumerating the elements of this dataset. (deprecated)

Returns:

An Iterator over the elements of this dataset.

`map`

map(
    map_func,
    num_parallel_calls=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.

For example:

a = Dataset.range(1, 6)  # ==> [ 1, 2, 3, 4, 5 ]

a.map(lambda x: x + 1)  # ==> [ 2, 3, 4, 5, 6 ]

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

# NOTE: The following examples use `{ ... }` to represent the
# contents of a dataset.
# Each element is a `tf.Tensor` object.
a = { 1, 2, 3, 4, 5 }
# `map_func` takes a single argument of type `tf.Tensor` with the same
# shape and dtype.
result = a.map(lambda x: ...)

# Each element is a tuple containing two `tf.Tensor` objects.
b = { (1, "foo"), (2, "bar"), (3, "baz") }
# `map_func` takes two arguments of type `tf.Tensor`.
result = b.map(lambda x_int, y_str: ...)

# Each element is a dictionary mapping strings to `tf.Tensor` objects.
c = { {"a": 1, "b": "foo"}, {"a": 2, "b": "bar"}, {"a": 3, "b": "baz"} }
# `map_func` takes a single argument of type `dict` with the same keys as
# the elements.
result = c.map(lambda d: ...)

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

# `map_func` returns a scalar `tf.Tensor` of type `tf.float32`.
def f(...):
  return tf.constant(37.0)
result = dataset.map(f)
result.output_classes == tf.Tensor
result.output_types == tf.float32
result.output_shapes == []  # scalar

# `map_func` returns two `tf.Tensor` objects.
def g(...):
  return tf.constant(37.0), tf.constant(["Foo", "Bar", "Baz"])
result = dataset.map(g)
result.output_classes == (tf.Tensor, tf.Tensor)
result.output_types == (tf.float32, tf.string)
result.output_shapes == ([], [3])

# Python primitives, lists, and NumPy arrays are implicitly converted to
# `tf.Tensor`.
def h(...):
  return 37.0, ["Foo", "Bar", "Baz"], np.array([1.0, 2.0] dtype=np.float64)
result = dataset.map(h)
result.output_classes == (tf.Tensor, tf.Tensor, tf.Tensor)
result.output_types == (tf.float32, tf.string, tf.float64)
result.output_shapes == ([], [3], [2])

# `map_func` can return nested structures.
def i(...):
  return {"a": 37.0, "b": [42, 16]}, "foo"
result.output_classes == ({"a": tf.Tensor, "b": tf.Tensor}, tf.Tensor)
result.output_types == ({"a": tf.float32, "b": tf.int32}, tf.string)
result.output_shapes == ({"a": [], "b": [2]}, [])

In addition to tf.Tensor objects, map_func can accept as arguments and return tf.SparseTensor objects.

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 two 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).

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.experimental.AUTOTUNE is used, then the number of parallel calls is set dynamically based on available CPU.

Returns:

Dataset: A Dataset.

`map_with_legacy_function`

map_with_legacy_function(
    map_func,
    num_parallel_calls=None
)

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

NOTE: This is an escape hatch for existing uses of map that do not work with V2 functions. New uses are strongly discouraged and existing uses should migrate to map as this method will be removed in V2.

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.experimental.AUTOTUNE is used, then the number of parallel calls is set dynamically based on available CPU.

Returns:

Dataset: A Dataset.

`options`

options()

`padded_batch`

padded_batch(
    batch_size,
    padded_shapes,
    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 tensors in 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 padding_shapes. The padding_shapes argument determines the resulting shape for each dimension of each component in an output element:

If the dimension is a constant (e.g. tf.compat.v1.Dimension(37)), the component will be padded out to that length in that dimension.
If the dimension is unknown (e.g. tf.compat.v1.Dimension(None)), the component will be padded out to the maximum length of all elements in that dimension.

See also tf.data.experimental.dense_to_sparse_batch, which combines elements that may have different shapes into a tf.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: 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 (e.g. tf.compat.v1.Dimension(None) in a tf.TensorShape or -1 in a tensor-like object) will be padded to the maximum size of that dimension in each batch.
padding_values: (Optional.) A nested structure of scalar-shaped tf.Tensor, representing the padding values to use for the respective components. Defaults are 0 for numeric types and the empty string for string types.
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.

`prefetch`

prefetch(buffer_size)

Creates a Dataset that prefetches elements from this dataset.

Note that if the dataset was batched using Dataset.batch, each element is a batch and this operation will prefetch buffer_size batches.

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`

range(*args)

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

For example:

Dataset.range(5) == [0, 1, 2, 3, 4]
Dataset.range(2, 5) == [2, 3, 4]
Dataset.range(1, 5, 2) == [1, 3]
Dataset.range(1, 5, -2) == []
Dataset.range(5, 1) == []
Dataset.range(5, 1, -2) == [5, 3]

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, stop = args[2]

Returns:

Dataset: A RangeDataset.

Raises:

ValueError: if len(args) == 0.

`reduce`

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.

For example:

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

Args:

initial_state: A nested structure of tensors, 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 nested structure of tensors. The structure of new_state must match the structure of initial_state.

Returns:

A nested structure of tf.Tensor objects, corresponding to the final state of the transformation.

`repeat`

repeat(count=None)

Repeats this dataset count times.

NOTE: If this dataset is a function of global state (e.g. a random number generator), then different repetitions may produce different elements.

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`

shard(
    num_shards,
    index
)

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

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 skip 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`

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.

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.compat.v1.set_random_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`

skip(count)

Creates a Dataset that skips count elements from this dataset.

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`

take(count)

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

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.

`window`

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

For example, letting {...} to represent a Dataset:

tf.data.Dataset.range(7).window(2) produces { {0, 1}, {2, 3}, {4, 5}, {6}}
tf.data.Dataset.range(7).window(3, 2, 1, True) produces { {0, 1, 2}, {2, 3, 4}, {4, 5, 6}}
tf.data.Dataset.range(7).window(3, 1, 2, True) produces { {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.

For example:

tf.data.Dataset.from_tensor_slices((range(4), range(4)).window(2) produces {({0, 1}, {0, 1}), ({2, 3}, {2, 3})}
tf.data.Dataset.from_tensor_slices({"a": range(4)}).window(2) produces { {"a": {0, 1}}, {"a": {2, 3}}}

Args:

size: A tf.int64 scalar tf.Tensor, representing the number of elements of the input dataset to combine into a window.
shift: (Optional.) A tf.int64 scalar tf.Tensor, representing the forward shift of the sliding window in each iteration. Defaults to size.
stride: (Optional.) A tf.int64 scalar tf.Tensor, representing the stride of the input elements in the sliding window.
drop_remainder: (Optional.) A tf.bool scalar tf.Tensor, representing whether a window should be dropped in case its size is smaller than window_size.

Returns:

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

`with_options`

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.

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`

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 an arbitrary nested structure of Dataset objects. For example:

a = Dataset.range(1, 4)  # ==> [ 1, 2, 3 ]
b = Dataset.range(4, 7)  # ==> [ 4, 5, 6 ]
c = Dataset.range(7, 13).batch(2)  # ==> [ [7, 8], [9, 10], [11, 12] ]
d = Dataset.range(13, 15)  # ==> [ 13, 14 ]

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

# The `datasets` argument may contain an arbitrary number of
# datasets.
Dataset.zip((a, b, c))  # ==> [ (1, 4, [7, 8]),
                        #       (2, 5, [9, 10]),
                        #       (3, 6, [11, 12]) ]

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

Args:

datasets: A nested structure of datasets.

Returns:

Dataset: A Dataset.

tf.data.experimental.RandomDataset

Class RandomDataset

Aliases:

__init__

Properties

output_classes

Returns:

output_shapes

Returns:

output_types

Returns:

Methods

__iter__

apply

For example:

Args:

Returns:

batch

Args:

Returns:

cache

Args:

Returns:

concatenate

Args:

Returns:

enumerate

For example:

Args:

Returns:

filter

Args:

Returns:

filter_with_legacy_function

Args:

Returns:

flat_map

Args:

Returns:

from_generator

For example:

Args:

Returns:

from_sparse_tensor_slices

Args:

Returns:

from_tensor_slices

Args:

Returns:

from_tensors

Args:

Returns:

interleave

For example:

Args:

Returns:

list_files

Example:

Args:

Returns:

make_initializable_iterator

Args:

Returns:

Raises:

make_one_shot_iterator

Returns:

map

For example:

Args:

Returns:

map_with_legacy_function

Args:

Returns:

options

padded_batch

Args:

Returns:

prefetch

Args:

Returns:

Class `RandomDataset`

`init`

`output_classes`

`output_shapes`

`output_types`

`iter`

`apply`

`batch`

`cache`

`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`

`window`

`with_options`

`zip`