tf.compat.v2.data.Dataset

Represents a potentially large set of elements.

Inherits From: CheckpointableBase

tf.compat.v2.data.Dataset(
    variant_tensor
)

A Dataset can be used to represent an input pipeline as a collection of elements and a "logical plan" of transformations that act on those elements.

A dataset contains elements that each have the same (nested) structure and the individual components of the structure can be of any type representable by tf.TypeSpec, including tf.Tensor, tf.data.Dataset, tf.SparseTensor, tf.RaggedTensor, or tf.TensorArray.

Example elements:

# Integer element
a = 1
# Float element
b = 2.0
# Tuple element with 2 components
c = (1, 2)
# Dict element with 3 components
d = {"a": (2, 2), "b": 3}
# Element containing a dataset
e = tf.data.Dataset.from_element(10)

Args
`variant_tensor`	A DT_VARIANT tensor that represents the dataset.

Attributes
`element_spec`	The type specification of an element of this 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.

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`

View source

batch(
    batch_size, drop_remainder=False
)

Combines consecutive elements of this dataset into batches.

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

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

Returns
`Dataset`	A `Dataset`.

`cache`

View source

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 elements in this Dataset. If a filename is not provided, the dataset will be cached in memory.

Returns
`Dataset`	A `Dataset`.

`concatenate`

View source

concatenate(
    dataset
)

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

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

# The input dataset and dataset to be concatenated should have the 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`

View source

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`

View source

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 dataset element to a boolean.

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:

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 dataset element to a dataset.

Returns
`Dataset`	A `Dataset`.

`from_generator`

View source

@staticmethod
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 supports 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]))

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

Returns
`Dataset`	A `Dataset`.

`from_tensor_slices`

View source

@staticmethod
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 dataset element, with each component having the same size in the 0th dimension.

Returns
`Dataset`	A `Dataset`.

`from_tensors`

View source

@staticmethod
from_tensors(
    tensors
)

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

Args
`tensors`	A dataset element.

Returns
`Dataset`	A `Dataset`.

`interleave`

View source

interleave(
    map_func, cycle_length=AUTOTUNE, 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]

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 specified, the value will be derived from the number of available CPU cores. If the `num_parallel_calls` argument is set to `tf.data.experimental.AUTOTUNE`, the `cycle_length` argument also 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.
`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`

View source

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

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

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.

`map`

View source

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]}, [])

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 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). 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) -> str:
    return t.numpy().decode('utf-8').upper()

d.map(lambda x: tf.py_function(func=upper_case_fn,
      inp=[x], Tout=tf.string))  # ==> [ "HELLO", "WORLD" ]

Args
`map_func`	A function mapping a dataset element to another dataset element.
`num_parallel_calls`	(Optional.) A `tf.int32` scalar `tf.Tensor`, representing the number elements to process asynchronously in parallel. If not specified, elements will be processed sequentially. If the value `tf.data.experimental.AUTOTUNE` is used, then the number of parallel calls is set dynamically based on available CPU.

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, 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 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`

View source

prefetch(
    buffer_size
)

Creates a Dataset that prefetches elements from this dataset.

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

Returns
`Dataset`	A `Dataset`.

`range`

View source

@staticmethod
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`

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.

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`	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 count times.

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.

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`

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.

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`

View source

skip(
    count
)

Creates a Dataset that skips count elements from this dataset.

tf.compat.v2.data.Dataset

Example elements:

Args

Attributes

Methods

apply

For example:

batch

cache

concatenate

enumerate

For example:

filter

flat_map

from_generator

For example:

from_tensor_slices

from_tensors

interleave

For example:

list_files

Example:

map

For example:

options

padded_batch

prefetch

range

For example:

reduce

For example:

repeat

shard

Important caveats:

shuffle

skip

`apply`

`batch`

`cache`

`concatenate`

`enumerate`

`filter`

`flat_map`

`from_generator`

`from_tensor_slices`

`from_tensors`

`interleave`

`list_files`

`map`

`options`

`padded_batch`

`prefetch`

`range`

`reduce`

`repeat`

`shard`

`shuffle`

`skip`