tf.data.Dataset

TensorFlow 1 version

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Represents a potentially large set of elements.

tf.data.Dataset(
    variant_tensor
)

The tf.data.Dataset API supports writing descriptive and efficient input pipelines. Dataset usage follows a common pattern:

1. Create a source dataset from your input data.
2. Apply dataset transformations to preprocess the data.
3. Iterate over the dataset and process the elements.

Iteration happens in a streaming fashion, so the full dataset does not need to fit into memory.

Source Datasets:

The simplest way to create a dataset is to create it from a python list:

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) 

To process lines from files, use tf.data.TextLineDataset:

dataset = tf.data.TextLineDataset(["file1.txt", "file2.txt"]) 

To process records written in the TFRecord format, use TFRecordDataset:

dataset = tf.data.TFRecordDataset(["file1.tfrecords", "file2.tfrecords"]) 

To create a dataset of all files matching a pattern, use tf.data.Dataset.list_files:

dataset = tf.data.Dataset.list_files("/path/*.txt")  # doctest: +SKIP 

See tf.data.FixedLengthRecordDataset and tf.data.Dataset.from_generator for more ways to create datasets.

Transformations:

Once you have a dataset, you can apply transformations to prepare the data for your model:

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) 
dataset = dataset.map(lambda x: x*2) 
list(dataset.as_numpy_iterator()) 
[2, 4, 6] 

Common Terms:

Element: A single output from calling next() on a dataset iterator. Elements may be nested structures containing multiple components. For example, the element (1, (3, "apple")) has one tuple nested in another tuple. The components are 1, 3, and "apple". Component: The leaf in the nested structure of an element.

Supported types:

Elements can be nested structures of tuples, named tuples, and dictionaries. Element components can be of any type representable by tf.TypeSpec, including tf.Tensor, tf.data.Dataset, tf.SparseTensor, tf.RaggedTensor, and tf.TensorArray.

a = 1 # Integer element 
b = 2.0 # Float element 
c = (1, 2) # Tuple element with 2 components 
d = {"a": (2, 2), "b": 3} # Dict element with 3 components 
Point = collections.namedtuple("Point", ["x", "y"]) # doctest: +SKIP 
e = Point(1, 2) # Named tuple # doctest: +SKIP 
f = tf.data.Dataset.range(10) # Dataset element 

Args:

• variant_tensor: A DT_VARIANT tensor that represents the dataset.

Attributes:

• element_spec: The type specification of an element of this dataset.

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

Methods

__iter__

View source

__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 not inside of tf.function and not executing eagerly.

apply

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apply(
    transformation_func
)

Applies a transformation function to this dataset.

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

dataset = tf.data.Dataset.range(100) 
def dataset_fn(ds): 
  return ds.filter(lambda x: x < 5) 
dataset = dataset.apply(dataset_fn) 
list(dataset.as_numpy_iterator()) 
[0, 1, 2, 3, 4] 

Args:

• transformation_func: A function that takes one Dataset argument and returns a Dataset.

Returns:

• Dataset: The Dataset returned by applying transformation_func to this dataset.

as_numpy_iterator

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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 

Returns:

An iterable over the elements of the dataset, with their tensors converted to numpy arrays.

Raises:

• TypeError: if an element contains a non-Tensor value.
• RuntimeError: if eager execution is not enabled.

batch

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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.

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

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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.

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] 

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

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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'> 

Args:

• dataset: Dataset to be concatenated.

Returns:

• Dataset: A Dataset.

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)) 

Args:

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

Returns:

• Dataset: A Dataset.

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] 

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:

dataset = Dataset.from_tensor_slices([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) 
dataset = dataset.flat_map(lambda x: Dataset.from_tensor_slices(x)) 
list(dataset.as_numpy_iterator()) 
[1, 2, 3, 4, 5, 6, 7, 8, 9] 

tf.data.Dataset.interleave() is a generalization of flat_map, since flat_map produces the same output as tf.data.Dataset.interleave(cycle_length=1)

Args:

• map_func: A function mapping a dataset element to a dataset.

Returns:

• Dataset: A Dataset.

from_generator

View source

@staticmethod
from_generator(
    generator, output_types, 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.

import itertools 
 
def gen(): 
  for i in itertools.count(1): 
    yield (i, [1] * i) 
 
dataset = tf.data.Dataset.from_generator( 
     gen, 
     (tf.int64, tf.int64), 
     (tf.TensorShape([]), tf.TensorShape([None]))) 
 
list(dataset.take(3).as_numpy_iterator()) 
[(1, array([1])), (2, array([1, 1])), (3, array([1, 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.

The given tensors are sliced along their first dimension. This operation preserves the structure of the input tensors, removing the first dimension of each tensor and using it as the dataset dimension. All input tensors must have the same size in their first dimensions.

# Slicing a 1D tensor produces scalar tensor elements. 
dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) 
list(dataset.as_numpy_iterator()) 
[1, 2, 3] 

# Slicing a 2D tensor produces 1D tensor elements. 
dataset = tf.data.Dataset.from_tensor_slices([[1, 2], [3, 4]]) 
list(dataset.as_numpy_iterator()) 
[array([1, 2], dtype=int32), array([3, 4], dtype=int32)] 

# Slicing a tuple of 1D tensors produces tuple elements containing 
# scalar tensors. 
dataset = tf.data.Dataset.from_tensor_slices(([1, 2], [3, 4], [5, 6])) 
list(dataset.as_numpy_iterator()) 
[(1, 3, 5), (2, 4, 6)] 

# Dictionary structure is also preserved. 
dataset = tf.data.Dataset.from_tensor_slices({"a": [1, 2], "b": [3, 4]}) 
list(dataset.as_numpy_iterator()) == [{'a': 1, 'b': 3}, 
                                      {'a': 2, 'b': 4}] 
True 

# Two tensors can be combined into one Dataset object. 
features = tf.constant([[1, 3], [2, 1], [3, 3]]) # ==> 3x2 tensor 
labels = tf.constant(['A', 'B', 'A']) # ==> 3x1 tensor 
dataset = Dataset.from_tensor_slices((features, labels)) 
# Both the features and the labels tensors can be converted 
# to a Dataset object separately and combined after. 
features_dataset = Dataset.from_tensor_slices(features) 
labels_dataset = Dataset.from_tensor_slices(labels) 
dataset = Dataset.zip((features_dataset, labels_dataset)) 
# A batched feature and label set can be converted to a Dataset 
# in similar fashion. 
batched_features = tf.constant([[[1, 3], [2, 3]], 
                                [[2, 1], [1, 2]], 
                                [[3, 3], [3, 2]]], shape=(3, 2, 2)) 
batched_labels = tf.constant([['A', 'A'], 
                              ['B', 'B'], 
                              ['A', 'B']], shape=(3, 2, 1)) 
dataset = Dataset.from_tensor_slices((batched_features, batched_labels)) 
for element in dataset.as_numpy_iterator(): 
  print(element) 
(array([[1, 3], 
       [2, 3]], dtype=int32), array([[b'A'], 
       [b'A']], dtype=object)) 
(array([[2, 1], 
       [1, 2]], dtype=int32), array([[b'B'], 
       [b'B']], dtype=object)) 
(array([[3, 3], 
       [3, 2]], dtype=int32), array([[b'A'], 
       [b'B']], dtype=object)) 

Note that if tensors contains a NumPy array, and eager execution is not enabled, the values will be embedded in the graph as one or more tf.constant operations. For large datasets (> 1 GB), this can waste memory and run into byte limits of graph serialization. If tensors contains one or more large NumPy arrays, consider the alternative described in this guide.

Args:

• tensors: A dataset element, with each component having the same size in the first dimension.

Returns:

• Dataset: A Dataset.

from_tensors

View source

@staticmethod
from_tensors(
    tensors
)

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

from_tensors produces a dataset containing only a single element. To slice the input tensor into multiple elements, use from_tensor_slices instead.

dataset = tf.data.Dataset.from_tensors([1, 2, 3]) 
list(dataset.as_numpy_iterator()) 
[array([1, 2, 3], dtype=int32)] 
dataset = tf.data.Dataset.from_tensors(([1, 2, 3], 'A')) 
list(dataset.as_numpy_iterator()) 
[(array([1, 2, 3], dtype=int32), b'A')] 

# You can use `from_tensors` to produce a dataset which repeats 
# the same example many times. 
example = tf.constant([1,2,3]) 
dataset = tf.data.Dataset.from_tensors(example).repeat(2) 
list(dataset.as_numpy_iterator()) 
[array([1, 2, 3], dtype=int32), array([1, 2, 3], dtype=int32)] 

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.

Returns:

• Dataset: A Dataset.

interleave

View source

interleave(
    map_func, cycle_length=AUTOTUNE, block_length=1, num_parallel_calls=None,
    deterministic=None
)

Maps map_func across this dataset, and interleaves the results.

For example, you can use Dataset.interleave() to process many input files concurrently:

# Preprocess 4 files concurrently, and interleave blocks of 16 records 
# from each file. 
filenames = ["/var/data/file1.txt", "/var/data/file2.txt", 
             "/var/data/file3.txt", "/var/data/file4.txt"] 
dataset = tf.data.Dataset.from_tensor_slices(filenames) 
def parse_fn(filename): 
  return tf.data.Dataset.range(10) 
dataset = dataset.interleave(lambda x: 
    tf.data.TextLineDataset(x).map(parse_fn, num_parallel_calls=1), 
    cycle_length=4, block_length=16) 

The cycle_length and block_length arguments control the order in which elements are produced. cycle_length controls the number of input elements that are processed concurrently. If you set cycle_length to 1, this transformation will handle one input element at a time, and will produce identical results to tf.data.Dataset.flat_map. In general, this transformation will apply map_func to cycle_length input elements, open iterators on the returned Dataset objects, and cycle through them producing block_length consecutive elements from each iterator, and consuming the next input element each time it reaches the end of an iterator.

For example:

dataset = Dataset.range(1, 6)  # ==> [ 1, 2, 3, 4, 5 ] 
# NOTE: New lines indicate "block" boundaries. 
dataset = dataset.interleave( 
    lambda x: Dataset.from_tensors(x).repeat(6), 
    cycle_length=2, block_length=4) 
list(dataset.as_numpy_iterator()) 
[1, 1, 1, 1, 
 2, 2, 2, 2, 
 1, 1, 
 2, 2, 
 3, 3, 3, 3, 
 4, 4, 4, 4, 
 3, 3, 
 4, 4, 
 5, 5, 5, 5, 
 5, 5] 

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

filenames = ["/var/data/file1.txt", "/var/data/file2.txt", 
             "/var/data/file3.txt", "/var/data/file4.txt"] 
dataset = tf.data.Dataset.from_tensor_slices(filenames) 
dataset = dataset.interleave(lambda x: tf.data.TFRecordDataset(x), 
    cycle_length=4, num_parallel_calls=tf.data.experimental.AUTOTUNE, 
    deterministic=False)