LostTech.TensorFlow : API Documentation

Type SqlDataset

Namespace tensorflow.data.experimental

Parent DatasetV1Adapter

Interfaces ISqlDataset

A `Dataset` consisting of the results from a SQL query.

Methods

Properties

Public instance methods

object enumerate(int start)

object enumerate(IGraphNodeBase start)

object enumerate_dyn(ImplicitContainer<T> start)

object repeat(object count)

Repeats this dataset `count` times.

NOTE: If this dataset is a function of global state (e.g. a random number generator), then different repetitions may produce different elements.
Parameters
object count
(Optional.) A tf.int64 scalar tf.Tensor, representing the number of times the dataset should be repeated. The default behavior (if `count` is `None` or `-1`) is for the dataset be repeated indefinitely.
Returns
object

object repeat_dyn(object count)

Repeats this dataset `count` times.

NOTE: If this dataset is a function of global state (e.g. a random number generator), then different repetitions may produce different elements.
Parameters
object count
(Optional.) A tf.int64 scalar tf.Tensor, representing the number of times the dataset should be repeated. The default behavior (if `count` is `None` or `-1`) is for the dataset be repeated indefinitely.
Returns
object

object shuffle(int buffer_size, int seed, Nullable<bool> 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
int buffer_size
A tf.int64 scalar tf.Tensor, representing the number of elements from this dataset from which the new dataset will sample.
int 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.
Nullable<bool> 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 shuffle(int buffer_size, IGraphNodeBase seed, Nullable<bool> 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
int buffer_size
A tf.int64 scalar tf.Tensor, representing the number of elements from this dataset from which the new dataset will sample.
IGraphNodeBase 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.
Nullable<bool> 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 shuffle(IGraphNodeBase buffer_size, int seed, Nullable<bool> 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
IGraphNodeBase buffer_size
A tf.int64 scalar tf.Tensor, representing the number of elements from this dataset from which the new dataset will sample.
int 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.
Nullable<bool> 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 shuffle(IGraphNodeBase buffer_size, IGraphNodeBase seed, Nullable<bool> 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
IGraphNodeBase buffer_size
A tf.int64 scalar tf.Tensor, representing the number of elements from this dataset from which the new dataset will sample.
IGraphNodeBase 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.
Nullable<bool> 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

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;