LostTech.TensorFlow : API Documentation

Type FixedLengthRecordDataset

Namespace tensorflow.data

Parent DatasetV1Adapter

Interfaces IFixedLengthRecordDataset

A `Dataset` of fixed-length records from one or more binary files.

Methods

Properties

Public instance methods

object cache(string filename)

Caches the elements in this dataset.
Parameters
string 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
object

object cache_dyn(ImplicitContainer<T> filename)

Caches the elements in this dataset.
Parameters
ImplicitContainer<T> 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
object

object shuffle_dyn(object buffer_size, object seed, object reshuffle_each_iteration)

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.
Parameters
object buffer_size
A tf.int64 scalar tf.Tensor, representing the number of elements from this dataset from which the new dataset will sample.
object 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.
object reshuffle_each_iteration
(Optional.) A boolean, which if true indicates that the dataset should be pseudorandomly reshuffled each time it is iterated over. (Defaults to `True`.)
Returns
object

object take(IEnumerable<int> count)

Creates a `Dataset` with at most `count` elements from this dataset.
Parameters
IEnumerable<int> count
A tf.int64 scalar tf.Tensor, representing the number of elements of this dataset that should be taken to form the new dataset. If `count` is -1, or if `count` is greater than the size of this dataset, the new dataset will contain all elements of this dataset.
Returns
object

Public properties

object element_spec get;

object element_spec_dyn get;

object output_classes get;

object output_classes_dyn get;

object output_shapes get;

object output_shapes_dyn get;

object output_types get;

object output_types_dyn get;

object PythonObject get;