tf.data.experimental.RandomDataset

A Dataset of pseudorandom values.

Inherits From: Dataset

tf.data.experimental.RandomDataset(
    seed=None
)

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

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]

as_numpy_iterator

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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

batch

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batch(
    batch_size, drop_remainder=False
)

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.

cache

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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.

Note: For the cache to be finalized, the input dataset must be iterated through in its entirety. Otherwise, subsequent iterations will not use cached data.

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

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

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

Note: cache will produce exactly the same elements during each iteration through the dataset. If you wish to randomize the iteration order, make sure to call shuffle after calling cache.

cardinality

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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

concatenate

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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 the same
# nested structures and output types.
c = tf.data.Dataset.zip((a, b))
a.concatenate(c)
Traceback (most recent call last):
TypeError: Two datasets to concatenate have different types
<dtype: 'int64'> and (tf.int64, tf.int64)
d = tf.data.Dataset.from_tensor_slices(["a", "b", "c"])
a.concatenate(d)
Traceback (most recent call last):
TypeError: Two datasets to concatenate have different types
<dtype: 'int64'> and <dtype: 'string'>

enumerate

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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))

filter

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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]

flat_map

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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)

from_generator

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@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, whichiver 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 with the types defined by output_types and with the shapes which are either unknown or defined by output_shapes.

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

from_tensor_slices

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@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.

from_tensors

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@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)]

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.

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]

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

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)

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.

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

map

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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. For example, adding 1 to each element, or projecting a subset of element components.

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

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

dataset = Dataset.range(5)
# `map_func` takes a single argument of type `tf.Tensor` with the same
# shape and dtype.
result = dataset.map(lambda x: x + 1)

# Each element is a tuple containing two `tf.Tensor` objects.
elements = [(1, "foo"), (2, "bar"), (3, "baz")]
dataset = tf.data.Dataset.from_generator(
    lambda: elements, (tf.int32, tf.string))
# `map_func` takes two arguments of type `tf.Tensor`. This function
# projects out just the first component.
result = dataset.map(lambda x_int, y_str: x_int)
list(result.as_numpy_iterator())
[1, 2, 3]

# Each element is a dictionary mapping strings to `tf.Tensor` objects.
elements =  ([{"a": 1, "b": "foo"},
              {"a": 2, "b": "bar"},
              {"a": 3, "b": "baz"}])
dataset = tf.data.Dataset.from_generator(
    lambda: elements, {"a": tf.int32, "b": tf.string})
# `map_func` takes a single argument of type `dict` with the same keys
# as the elements.
result = dataset.map(lambda d: str(d["a"]) + d["b"])

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

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

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

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

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

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

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

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

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

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

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

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

options

View source

options()

Returns the options for this dataset and its inputs.

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.

prefetch

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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.

Note: Like other Dataset methods, prefetch operates on the elements of the input dataset. It has no concept of examples vs. batches. examples.prefetch(2) will prefetch two elements (2 examples), while examples.batch(20).prefetch(2) will prefetch 2 elements (2 batches, of 20 examples each).

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

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]

reduce

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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

repeat

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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]

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

shard

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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)

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)  # doctest: +SKIP
[1, 0, 2, 1, 2, 0]

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

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

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

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

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]

take

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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]

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]

Note: unbatch requires a data copy to slice up the batched tensor into smaller, unbatched tensors. When optimizing performance, try to avoid unnecessary usage of unbatch.

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]}

with_options

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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)

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 an arbitrary nested structure of Dataset objects.

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

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

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

__bool__

View source

__bool__()

__iter__

View source

__iter__()

Creates an iterator for elements of this dataset.

The returned iterator implements the Python Iterator protocol.

__len__

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__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.

__nonzero__

View source

__nonzero__()