# tf.contrib.data.RandomDataset

## Class RandomDataset

Inherits From: Dataset

A Dataset of pseudorandom values.

## Methods

### __init__

__init__(seed=None)


A Dataset of pseudorandom values.

### __iter__

__iter__()


Creates an Iterator for enumerating the elements of this dataset.

The returned iterator implements the Python iterator protocol and therefore can only be used in eager mode.

#### Returns:

An Iterator over the elements of this dataset.

#### Raises:

• RuntimeError: If eager execution is not enabled.

### apply

apply(transformation_func)


Apply 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 its 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.

# NOTE: The following examples use { ... } to represent the
# contents of a dataset.
a = { 1, 2, 3 }
b = { 4, 5, 6, 7 }

# Input dataset and dataset to be concatenated should have same
# nested structures and output types.
# c = { (8, 9), (10, 11), (12, 13) }
# d = { 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.

### filter

filter(predicate)


Filters this dataset according to predicate.

#### 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.

#### 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

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

ds = Dataset.from_generator(
gen, (tf.int64, tf.int64), (tf.TensorShape([]), tf.TensorShape([None])))
value = ds.make_one_shot_iterator().get_next()

sess.run(value)  # (1, array([1]))
sess.run(value)  # (2, array([1, 1]))


NOTE: The current implementation of Dataset.from_generator() uses tf.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)

THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.data.Dataset.from_tensor_slices().

#### 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.

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.

#### Returns:

• Dataset: A Dataset.

### interleave

interleave(
map_func,
cycle_length,
block_length=1
)


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:

# NOTE: The following examples use { ... } to represent the
# contents of a dataset.
a = { 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.

#### Returns:

• Dataset: A Dataset.

### list_files

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


A dataset of all files matching a pattern.

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

#### 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.

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.

#### 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:

# NOTE: The following examples use { ... } to represent the
# contents of a dataset.
a = { 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:

# Each element is a <a href="../../../tf/Tensor"><code>tf.Tensor</code></a> object.
a = { 1, 2, 3, 4, 5 }
# map_func takes a single argument of type <a href="../../../tf/Tensor"><code>tf.Tensor</code></a> with the same
# shape and dtype.
result = a.map(lambda x: ...)

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

# Each element is a dictionary mapping strings to <a href="../../../tf/Tensor"><code>tf.Tensor</code></a> 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 <a href="../../../tf/Tensor"><code>tf.Tensor</code></a> of type <a href="../../../tf/float32"><code>tf.float32</code></a>.
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 <a href="../../../tf/Tensor"><code>tf.Tensor</code></a> 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
# <a href="../../../tf/Tensor"><code>tf.Tensor</code></a>.
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.

#### 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 in parallel. If not specified, elements will be processed sequentially.

#### Returns:

• Dataset: A Dataset.

### padded_batch

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

See also tf.contrib.data.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.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 its 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.

#### 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: follow 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.

### 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(FLAGS.input_file)
d = d.shard(FLAGS.num_workers, FLAGS.worker_index)
d = d.repeat(FLAGS.num_epochs)
d = d.shuffle(FLAGS.shuffle_buffer_size)


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(FLAGS.pattern)
d = d.shard(FLAGS.num_workers, FLAGS.worker_index)
d = d.repeat(FLAGS.num_epochs)
d = d.shuffle(FLAGS.shuffle_buffer_size)
d = d.interleave(tf.data.TFRecordDataset,


#### Returns:

• Dataset: A Dataset.

#### Raises:

• ValueError: if num_shards or index are illegal values. Note: error checking is done on a best-effort basis, and 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.

#### 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.

### 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:

# NOTE: The following examples use { ... } to represent the
# contents of a dataset.
a = { 1, 2, 3 }
b = { 4, 5, 6 }
c = { (7, 8), (9, 10), (11, 12) }
d = { 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.