(Optional.) A tf.string scalar evaluating to one of
"" (no compression), "ZLIB", or "GZIP".
buffer_size
(Optional.) A tf.int64 scalar denoting the number of bytes
to buffer. A value of 0 results in the default buffering values chosen
based on the compression type.
num_parallel_reads
(Optional.) A tf.int64 scalar representing the
number of files to read in parallel. If greater than one, the records of
files read in parallel are outputted in an interleaved order. If your
input pipeline is I/O bottlenecked, consider setting this parameter to a
value greater than one to parallelize the I/O. If None, files will be
read sequentially.
Attributes
element_spec
The type specification of an element of this dataset.
output_classes
Returns the class of each component of an element of this dataset. (deprecated)
output_shapes
Returns the shape of each component of an element of this dataset. (deprecated)
output_types
Returns the type of each component of an element of this dataset. (deprecated)
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.
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.
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.
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 ]
# 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.
Filters this dataset according to predicate. (deprecated)
Args
predicate
A function mapping a nested structure of tensors (having shapes
and types defined by self.output_shapes and self.output_types) to a
scalar tf.bool tensor.
Returns
Dataset
The Dataset containing the elements of this dataset for which
predicate is True.
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:
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.
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.
Creates a Dataset with a single element, comprising the given tensors.
Note that if tensors contains a NumPy array, and eager execution is not
enabled, the values will be embedded in the graph as one or more
tf.constant operations. For large datasets (> 1 GB), this can waste
memory and run into byte limits of graph serialization. If tensors
contains one or more large NumPy arrays, consider the alternative described
in this
guide.
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.
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.
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.
(Optional.) If non-empty, the returned iterator will be
shared under the given name across multiple sessions that share the same
devices (e.g. when using a remote server).
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.
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 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.
Maps map_func across the elements of this dataset. (deprecated)
Args
map_func
A function mapping a nested structure of tensors (having shapes
and types defined by self.output_shapes and self.output_types) to
another nested structure of tensors.
num_parallel_calls
(Optional.) A tf.int32 scalar tf.Tensor,
representing the number elements to process 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.
TensorFlow 2 version
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