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A Dataset
comprising records from one or more TFRecord files.
Inherits From: Dataset
tf.data.TFRecordDataset(
filenames, compression_type=None, buffer_size=None, num_parallel_reads=None
)
Args | |
---|---|
filenames
|
A tf.string tensor or tf.data.Dataset containing one or
more filenames.
|
compression_type
|
(Optional.) A tf.string scalar evaluating to one of
"" (no compression), "ZLIB" , or "GZIP" .
|
buffer_size
|
(Optional.) A tf.int64 scalar representing the number of
bytes in the read buffer. If your input pipeline is I/O bottlenecked,
consider setting this parameter to a value 1-100 MBs. If None , a
sensible default for both local and remote file systems is used.
|
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.
|
Raises | |
---|---|
TypeError
|
If any argument does not have the expected type. |
ValueError
|
If any argument does not have the expected shape. |
Attributes | |
---|---|
element_spec
|
The type specification of an element of this dataset.
|
Methods
apply
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
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
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
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 .
|
cardinality
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()