tf.contrib.data.LMDBDataset

Class `LMDBDataset`

A LMDB Dataset that reads the lmdb file.

`init`

__init__(filenames)

Create a LMDBDataset.

LMDBDataset allows a user to read data from a mdb file as (key value) pairs sequentially. For example:

tf.compat.v1.enable_eager_execution()

dataset = tf.contrib.lmdb.LMDBDataset("/foo/bar.mdb")

# Prints the (key, value) pairs inside a lmdb file.
for key, value in dataset:
  print(key, value)

Args: filenames: A tf.string tensor containing one or more filenames.

Properties

`element_spec`

The type specification of an element of this dataset.

Returns:

A nested structure of tf.TypeSpec objects matching the structure of an element of this dataset and specifying the type of individual components.

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`

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

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]))

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().

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

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

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]

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 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

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

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

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

Example:

If we had the following files on our filesystem: - /path/to/dir/a.txt - /path/to/dir/b.py - /path/to/dir/c.py If we pass "/path/to/dir/*.py" as the directory, the dataset would produce: - /path/to/dir/b.py - /path/to/dir/c.py

Args:

file_pattern: A string, a list of strings, or a tf.Tensor of string type (scalar or vector), representing the filename glob (i.e. shell wildcard) pattern(s) that will be matched.
shuffle: (Optional.) If True, the file names will be shuffled randomly. Defaults to True.
seed: (Optional.) A tf.int64 scalar tf.Tensor, representing the random seed that will be used to create the distribution. See tf.compat.v1.set_random_seed for behavior.

Returns:

Dataset: A Dataset of strings corresponding to file names.

`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:

tf.contrib.data.LMDBDataset

Class LMDBDataset

__init__

Properties

element_spec

Returns:

Methods

__iter__

Returns:

Raises:

apply

For example:

Args:

Returns:

batch

Args:

Returns:

cache

Args:

Returns:

concatenate

Args:

Returns:

enumerate

For example:

Args:

Returns:

filter

Args:

Returns:

flat_map

Args:

Returns:

from_generator

For example:

Args:

Returns:

from_tensor_slices

Args:

Returns:

from_tensors

Args:

Returns:

interleave

For example:

Args:

Returns:

list_files

Example:

Args:

Returns:

map

For example:

Class `LMDBDataset`

`init`

`element_spec`

`iter`

`apply`

`batch`

`cache`

`concatenate`

`enumerate`

`filter`

`flat_map`

`from_generator`

`from_tensor_slices`

`from_tensors`

`interleave`

`list_files`

`map`