# LostTech.TensorFlow : API Documentation

Type tf.train

Namespace tensorflow

### Public static methods

Adds a QueueRunner to a collection in the graph. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: To construct input pipelines, use the tf.data module.

When building a complex model that uses many queues it is often difficult to gather all the queue runners that need to be run. This convenience function allows you to add a queue runner to a well known collection in the graph.

The companion method start_queue_runners() can be used to start threads for all the collected queue runners.
##### Parameters
QueueRunner qr
A QueueRunner.
ImplicitContainer<T> collection
A GraphKey specifying the graph collection to add the queue runner to. Defaults to GraphKeys.QUEUE_RUNNERS.

Adds a QueueRunner to a collection in the graph. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: To construct input pipelines, use the tf.data module.

When building a complex model that uses many queues it is often difficult to gather all the queue runners that need to be run. This convenience function allows you to add a queue runner to a well known collection in the graph.

The companion method start_queue_runners() can be used to start threads for all the collected queue runners.
##### Parameters
object qr
A QueueRunner.
ImplicitContainer<T> collection
A GraphKey specifying the graph collection to add the queue runner to. Defaults to GraphKeys.QUEUE_RUNNERS.

#### voidassert_global_step(IEnumerable<object> global_step_tensor)

Asserts global_step_tensor is a scalar int Variable or Tensor.
##### Parameters
IEnumerable<object> global_step_tensor
Tensor to test.

#### voidassert_global_step(object global_step_tensor)

Asserts global_step_tensor is a scalar int Variable or Tensor.
##### Parameters
object global_step_tensor
Tensor to test.

#### voidbasic_train_loop(Supervisor supervisor, PythonFunctionContainer train_step_fn, Nullable<ValueTuple<Supervisor, string>> args, IDictionary<string, string> kwargs, string master)

Basic loop to train a model.

Calls train_step_fn in a loop to train a model. The function is called as: It is passed a tf.compat.v1.Session in addition to args and kwargs. The function typically runs one training step in the session.
##### Parameters
Supervisor supervisor
tf.compat.v1.train.Supervisor to run the training services.
PythonFunctionContainer train_step_fn
Callable to execute one training step. Called repeatedly as train_step_fn(session, *args **kwargs).
Nullable<ValueTuple<Supervisor, string>> args
Optional positional arguments passed to train_step_fn.
IDictionary<string, string> kwargs
Optional keyword arguments passed to train_step_fn.
string master
Master to use to create the training session. Defaults to "" which causes the session to be created in the local process.
Show Example
train_step_fn(session, *args, **kwargs)

#### objectbasic_train_loop_dyn(object supervisor, object train_step_fn, object args, object kwargs, ImplicitContainer<T> master)

Basic loop to train a model.

Calls train_step_fn in a loop to train a model. The function is called as: It is passed a tf.compat.v1.Session in addition to args and kwargs. The function typically runs one training step in the session.
##### Parameters
object supervisor
tf.compat.v1.train.Supervisor to run the training services.
object train_step_fn
Callable to execute one training step. Called repeatedly as train_step_fn(session, *args **kwargs).
object args
Optional positional arguments passed to train_step_fn.
object kwargs
Optional keyword arguments passed to train_step_fn.
ImplicitContainer<T> master
Master to use to create the training session. Defaults to "" which causes the session to be created in the local process.
Show Example
train_step_fn(session, *args, **kwargs)

#### objectbatch(IEnumerable<IGraphNodeBase> tensors, Nullable<int> batch_size, int num_threads, Nullable<int> capacity, bool enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, string shared_name, string name)

Creates batches of tensors in tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

The argument tensors can be a list or a dictionary of tensors. The value returned by the function will be of the same type as tensors.

This function is implemented using a queue. A QueueRunner for the queue is added to the current Graph's QUEUE_RUNNER collection.

If enqueue_many is False, tensors is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z]. The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

*N.B.:* If dynamic_pad is False, you must ensure that either (i) the shapes argument is passed, or (ii) all of the tensors in tensors must have fully-defined shapes. ValueError will be raised if neither of these conditions holds.

If dynamic_pad is True, it is sufficient that the *rank* of the tensors is known, but individual dimensions may have shape None. In this case, for each enqueue the dimensions with value None may have a variable length; upon dequeue, the output tensors will be padded on the right to the maximum shape of the tensors in the current minibatch. For numbers, this padding takes value 0. For strings, this padding is the empty string. See PaddingFIFOQueue for more info.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<IGraphNodeBase> tensors
The list or dictionary of tensors to enqueue.
Nullable<int> batch_size
The new batch size pulled from the queue.
The number of threads enqueuing tensors. The batching will be nondeterministic if num_threads > 1.
Nullable<int> capacity
An integer. The maximum number of elements in the queue.
bool enqueue_many
Whether each tensor in tensors is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(Optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same types as tensors (except if the input is a list of one element, then it returns a tensor, not a list).

#### objectbatch(IDictionary<string, string> tensors, Nullable<int> batch_size, int num_threads, Nullable<int> capacity, bool enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, string shared_name, string name)

Creates batches of tensors in tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

The argument tensors can be a list or a dictionary of tensors. The value returned by the function will be of the same type as tensors.

This function is implemented using a queue. A QueueRunner for the queue is added to the current Graph's QUEUE_RUNNER collection.

If enqueue_many is False, tensors is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z]. The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

*N.B.:* If dynamic_pad is False, you must ensure that either (i) the shapes argument is passed, or (ii) all of the tensors in tensors must have fully-defined shapes. ValueError will be raised if neither of these conditions holds.

If dynamic_pad is True, it is sufficient that the *rank* of the tensors is known, but individual dimensions may have shape None. In this case, for each enqueue the dimensions with value None may have a variable length; upon dequeue, the output tensors will be padded on the right to the maximum shape of the tensors in the current minibatch. For numbers, this padding takes value 0. For strings, this padding is the empty string. See PaddingFIFOQueue for more info.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IDictionary<string, string> tensors
The list or dictionary of tensors to enqueue.
Nullable<int> batch_size
The new batch size pulled from the queue.
The number of threads enqueuing tensors. The batching will be nondeterministic if num_threads > 1.
Nullable<int> capacity
An integer. The maximum number of elements in the queue.
bool enqueue_many
Whether each tensor in tensors is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(Optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same types as tensors (except if the input is a list of one element, then it returns a tensor, not a list).

#### objectbatch_dyn(object tensors, object batch_size, ImplicitContainer<T> num_threads, ImplicitContainer<T> capacity, ImplicitContainer<T> enqueue_many, object shapes, ImplicitContainer<T> dynamic_pad, ImplicitContainer<T> allow_smaller_final_batch, object shared_name, object name)

Creates batches of tensors in tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

The argument tensors can be a list or a dictionary of tensors. The value returned by the function will be of the same type as tensors.

This function is implemented using a queue. A QueueRunner for the queue is added to the current Graph's QUEUE_RUNNER collection.

If enqueue_many is False, tensors is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z]. The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

*N.B.:* If dynamic_pad is False, you must ensure that either (i) the shapes argument is passed, or (ii) all of the tensors in tensors must have fully-defined shapes. ValueError will be raised if neither of these conditions holds.

If dynamic_pad is True, it is sufficient that the *rank* of the tensors is known, but individual dimensions may have shape None. In this case, for each enqueue the dimensions with value None may have a variable length; upon dequeue, the output tensors will be padded on the right to the maximum shape of the tensors in the current minibatch. For numbers, this padding takes value 0. For strings, this padding is the empty string. See PaddingFIFOQueue for more info.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
object tensors
The list or dictionary of tensors to enqueue.
object batch_size
The new batch size pulled from the queue.
The number of threads enqueuing tensors. The batching will be nondeterministic if num_threads > 1.
ImplicitContainer<T> capacity
An integer. The maximum number of elements in the queue.
ImplicitContainer<T> enqueue_many
Whether each tensor in tensors is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
ImplicitContainer<T> allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional). If set, this queue will be shared under the given name across multiple sessions.
object name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same types as tensors (except if the input is a list of one element, then it returns a tensor, not a list).

#### objectbatch_join(IEnumerable<IDictionary<string, string>> tensors_list, IGraphNodeBase batch_size, int capacity, Nullable<int> enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, string shared_name, string name)

Runs a list of tensors to fill a queue to create batches of examples. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.batch().

WARNING: This function is nondeterministic, since it starts a separate thread for each tensor.

Enqueues a different list of tensors in different threads. Implemented using a queue -- a QueueRunner for the queue is added to the current Graph's QUEUE_RUNNER collection.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor x will be output as a tensor with shape [batch_size] + x.shape.

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. The slices of any input tensor x are treated as examples, and the output tensors will have shape [batch_size] + x.shape[1:].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

*N.B.:* If dynamic_pad is False, you must ensure that either (i) the shapes argument is passed, or (ii) all of the tensors in tensors_list must have fully-defined shapes. ValueError will be raised if neither of these conditions holds.

If dynamic_pad is True, it is sufficient that the *rank* of the tensors is known, but individual dimensions may have value None. In this case, for each enqueue the dimensions with value None may have a variable length; upon dequeue, the output tensors will be padded on the right to the maximum shape of the tensors in the current minibatch. For numbers, this padding takes value 0. For strings, this padding is the empty string. See PaddingFIFOQueue for more info.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<IDictionary<string, string>> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
IGraphNodeBase batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
Nullable<int> enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list_list[i].
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectbatch_join(IEnumerable<IDictionary<string, string>> tensors_list, IGraphNodeBase batch_size, int capacity, bool enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, string shared_name, string name)

Runs a list of tensors to fill a queue to create batches of examples. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.batch().

WARNING: This function is nondeterministic, since it starts a separate thread for each tensor.

Enqueues a different list of tensors in different threads. Implemented using a queue -- a QueueRunner for the queue is added to the current Graph's QUEUE_RUNNER collection.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor x will be output as a tensor with shape [batch_size] + x.shape.

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. The slices of any input tensor x are treated as examples, and the output tensors will have shape [batch_size] + x.shape[1:].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

*N.B.:* If dynamic_pad is False, you must ensure that either (i) the shapes argument is passed, or (ii) all of the tensors in tensors_list must have fully-defined shapes. ValueError will be raised if neither of these conditions holds.

If dynamic_pad is True, it is sufficient that the *rank* of the tensors is known, but individual dimensions may have value None. In this case, for each enqueue the dimensions with value None may have a variable length; upon dequeue, the output tensors will be padded on the right to the maximum shape of the tensors in the current minibatch. For numbers, this padding takes value 0. For strings, this padding is the empty string. See PaddingFIFOQueue for more info.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<IDictionary<string, string>> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
IGraphNodeBase batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
bool enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list_list[i].
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectbatch_join(IEnumerable<IDictionary<string, string>> tensors_list, int batch_size, int capacity, Nullable<int> enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, string shared_name, string name)

Runs a list of tensors to fill a queue to create batches of examples. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.batch().

WARNING: This function is nondeterministic, since it starts a separate thread for each tensor.

Enqueues a different list of tensors in different threads. Implemented using a queue -- a QueueRunner for the queue is added to the current Graph's QUEUE_RUNNER collection.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor x will be output as a tensor with shape [batch_size] + x.shape.

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. The slices of any input tensor x are treated as examples, and the output tensors will have shape [batch_size] + x.shape[1:].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

*N.B.:* If dynamic_pad is False, you must ensure that either (i) the shapes argument is passed, or (ii) all of the tensors in tensors_list must have fully-defined shapes. ValueError will be raised if neither of these conditions holds.

If dynamic_pad is True, it is sufficient that the *rank* of the tensors is known, but individual dimensions may have value None. In this case, for each enqueue the dimensions with value None may have a variable length; upon dequeue, the output tensors will be padded on the right to the maximum shape of the tensors in the current minibatch. For numbers, this padding takes value 0. For strings, this padding is the empty string. See PaddingFIFOQueue for more info.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<IDictionary<string, string>> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
int batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
Nullable<int> enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list_list[i].
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectbatch_join(IEnumerable<IDictionary<string, string>> tensors_list, int batch_size, int capacity, bool enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, string shared_name, string name)

Runs a list of tensors to fill a queue to create batches of examples. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.batch().

WARNING: This function is nondeterministic, since it starts a separate thread for each tensor.

Enqueues a different list of tensors in different threads. Implemented using a queue -- a QueueRunner for the queue is added to the current Graph's QUEUE_RUNNER collection.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor x will be output as a tensor with shape [batch_size] + x.shape.

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. The slices of any input tensor x are treated as examples, and the output tensors will have shape [batch_size] + x.shape[1:].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

*N.B.:* If dynamic_pad is False, you must ensure that either (i) the shapes argument is passed, or (ii) all of the tensors in tensors_list must have fully-defined shapes. ValueError will be raised if neither of these conditions holds.

If dynamic_pad is True, it is sufficient that the *rank* of the tensors is known, but individual dimensions may have value None. In this case, for each enqueue the dimensions with value None may have a variable length; upon dequeue, the output tensors will be padded on the right to the maximum shape of the tensors in the current minibatch. For numbers, this padding takes value 0. For strings, this padding is the empty string. See PaddingFIFOQueue for more info.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<IDictionary<string, string>> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
int batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
bool enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list_list[i].
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectbatch_join_dyn(object tensors_list, object batch_size, ImplicitContainer<T> capacity, ImplicitContainer<T> enqueue_many, object shapes, ImplicitContainer<T> dynamic_pad, ImplicitContainer<T> allow_smaller_final_batch, object shared_name, object name)

Runs a list of tensors to fill a queue to create batches of examples. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.batch().

WARNING: This function is nondeterministic, since it starts a separate thread for each tensor.

Enqueues a different list of tensors in different threads. Implemented using a queue -- a QueueRunner for the queue is added to the current Graph's QUEUE_RUNNER collection.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor x will be output as a tensor with shape [batch_size] + x.shape.

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. The slices of any input tensor x are treated as examples, and the output tensors will have shape [batch_size] + x.shape[1:].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

*N.B.:* If dynamic_pad is False, you must ensure that either (i) the shapes argument is passed, or (ii) all of the tensors in tensors_list must have fully-defined shapes. ValueError will be raised if neither of these conditions holds.

If dynamic_pad is True, it is sufficient that the *rank* of the tensors is known, but individual dimensions may have value None. In this case, for each enqueue the dimensions with value None may have a variable length; upon dequeue, the output tensors will be padded on the right to the maximum shape of the tensors in the current minibatch. For numbers, this padding takes value 0. For strings, this padding is the empty string. See PaddingFIFOQueue for more info.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
object tensors_list
A list of tuples or dictionaries of tensors to enqueue.
object batch_size
An integer. The new batch size pulled from the queue.
ImplicitContainer<T> capacity
An integer. The maximum number of elements in the queue.
ImplicitContainer<T> enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list_list[i].
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
ImplicitContainer<T> allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
object name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### boolcheckpoint_exists(Byte[] checkpoint_prefix)

Checks whether a V1 or V2 checkpoint exists with the specified prefix. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use standard file APIs to check for files with this prefix.

This is the recommended way to check if a checkpoint exists, since it takes into account the naming difference between V1 and V2 formats.
##### Parameters
Byte[] checkpoint_prefix
the prefix of a V1 or V2 checkpoint, with V2 taking priority. Typically the result of Saver.save() or that of tf.train.latest_checkpoint(), regardless of sharded/non-sharded or V1/V2.
##### Returns
bool
A bool, true if a checkpoint referred to by checkpoint_prefix exists.

#### boolcheckpoint_exists(string checkpoint_prefix)

Checks whether a V1 or V2 checkpoint exists with the specified prefix. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use standard file APIs to check for files with this prefix.

This is the recommended way to check if a checkpoint exists, since it takes into account the naming difference between V1 and V2 formats.
##### Parameters
string checkpoint_prefix
the prefix of a V1 or V2 checkpoint, with V2 taking priority. Typically the result of Saver.save() or that of tf.train.latest_checkpoint(), regardless of sharded/non-sharded or V1/V2.
##### Returns
bool
A bool, true if a checkpoint referred to by checkpoint_prefix exists.

#### boolcheckpoint_exists(IGraphNodeBase checkpoint_prefix)

Checks whether a V1 or V2 checkpoint exists with the specified prefix. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use standard file APIs to check for files with this prefix.

This is the recommended way to check if a checkpoint exists, since it takes into account the naming difference between V1 and V2 formats.
##### Parameters
IGraphNodeBase checkpoint_prefix
the prefix of a V1 or V2 checkpoint, with V2 taking priority. Typically the result of Saver.save() or that of tf.train.latest_checkpoint(), regardless of sharded/non-sharded or V1/V2.
##### Returns
bool
A bool, true if a checkpoint referred to by checkpoint_prefix exists.

#### boolcheckpoint_exists(IEnumerable<object> checkpoint_prefix)

Checks whether a V1 or V2 checkpoint exists with the specified prefix. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use standard file APIs to check for files with this prefix.

This is the recommended way to check if a checkpoint exists, since it takes into account the naming difference between V1 and V2 formats.
##### Parameters
IEnumerable<object> checkpoint_prefix
the prefix of a V1 or V2 checkpoint, with V2 taking priority. Typically the result of Saver.save() or that of tf.train.latest_checkpoint(), regardless of sharded/non-sharded or V1/V2.
##### Returns
bool
A bool, true if a checkpoint referred to by checkpoint_prefix exists.

#### IEnumerator<object>checkpoints_iterator(string checkpoint_dir, int min_interval_secs, double timeout, PythonFunctionContainer timeout_fn)

Continuously yield new checkpoint files as they appear.

The iterator only checks for new checkpoints when control flow has been reverted to it. This means it can miss checkpoints if your code takes longer to run between iterations than min_interval_secs or the interval at which new checkpoints are written.

The timeout argument is the maximum number of seconds to block waiting for a new checkpoint. It is used in combination with the timeout_fn as follows:

* If the timeout expires and no timeout_fn was specified, the iterator stops yielding. * If a timeout_fn was specified, that function is called and if it returns a true boolean value the iterator stops yielding. * If the function returns a false boolean value then the iterator resumes the wait for new checkpoints. At this point the timeout logic applies again.

This behavior gives control to callers on what to do if checkpoints do not come fast enough or stop being generated. For example, if callers have a way to detect that the training has stopped and know that no new checkpoints will be generated, they can provide a timeout_fn that returns True when the training has stopped. If they know that the training is still going on they return False instead.
##### Parameters
string checkpoint_dir
The directory in which checkpoints are saved.
int min_interval_secs
The minimum number of seconds between yielding checkpoints.
double timeout
The maximum number of seconds to wait between checkpoints. If left as None, then the process will wait indefinitely.
PythonFunctionContainer timeout_fn
Optional function to call after a timeout. If the function returns True, then it means that no new checkpoints will be generated and the iterator will exit. The function is called with no arguments.

#### objectcosine_decay(double learning_rate, int global_step, int decay_steps, double alpha, string name)

Applies cosine decay to the learning rate.

See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies a cosine decay function to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: Example usage:
##### Parameters
double learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
int global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation.
int decay_steps
A scalar int32 or int64 Tensor or a Python number. Number of steps to decay over.
double alpha
A scalar float32 or float64 Tensor or a Python number. Minimum learning rate value as a fraction of learning_rate.
string name
String. Optional name of the operation. Defaults to 'CosineDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
global_step = min(global_step, decay_steps)
cosine_decay = 0.5 * (1 + cos(pi * global_step / decay_steps))
decayed = (1 - alpha) * cosine_decay + alpha
decayed_learning_rate = learning_rate * decayed

#### objectcosine_decay_dyn(object learning_rate, object global_step, object decay_steps, ImplicitContainer<T> alpha, object name)

Applies cosine decay to the learning rate.

See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies a cosine decay function to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: Example usage:
##### Parameters
object learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
object global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation.
object decay_steps
A scalar int32 or int64 Tensor or a Python number. Number of steps to decay over.
ImplicitContainer<T> alpha
A scalar float32 or float64 Tensor or a Python number. Minimum learning rate value as a fraction of learning_rate.
object name
String. Optional name of the operation. Defaults to 'CosineDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
global_step = min(global_step, decay_steps)
cosine_decay = 0.5 * (1 + cos(pi * global_step / decay_steps))
decayed = (1 - alpha) * cosine_decay + alpha
decayed_learning_rate = learning_rate * decayed

#### objectcosine_decay_restarts(double learning_rate, int global_step, int first_decay_steps, double t_mul, double m_mul, double alpha, string name)

Applies cosine decay with restarts to the learning rate.

See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies a cosine decay function with restarts to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate while taking into account possible warm restarts. The learning rate multiplier first decays from 1 to alpha for first_decay_steps steps. Then, a warm restart is performed. Each new warm restart runs for t_mul times more steps and with m_mul times smaller initial learning rate.

Example usage:
##### Parameters
double learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
int global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation.
int first_decay_steps
A scalar int32 or int64 Tensor or a Python number. Number of steps to decay over.
double t_mul
A scalar float32 or float64 Tensor or a Python number. Used to derive the number of iterations in the i-th period
double m_mul
A scalar float32 or float64 Tensor or a Python number. Used to derive the initial learning rate of the i-th period:
double alpha
A scalar float32 or float64 Tensor or a Python number. Minimum learning rate value as a fraction of the learning_rate.
string name
String. Optional name of the operation. Defaults to 'SGDRDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
first_decay_steps = 1000
lr_decayed = cosine_decay_restarts(learning_rate, global_step,
first_decay_steps)

#### objectcosine_decay_restarts(double learning_rate, IGraphNodeBase global_step, int first_decay_steps, double t_mul, double m_mul, double alpha, string name)

Applies cosine decay with restarts to the learning rate.

See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies a cosine decay function with restarts to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate while taking into account possible warm restarts. The learning rate multiplier first decays from 1 to alpha for first_decay_steps steps. Then, a warm restart is performed. Each new warm restart runs for t_mul times more steps and with m_mul times smaller initial learning rate.

Example usage:
##### Parameters
double learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
IGraphNodeBase global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation.
int first_decay_steps
A scalar int32 or int64 Tensor or a Python number. Number of steps to decay over.
double t_mul
A scalar float32 or float64 Tensor or a Python number. Used to derive the number of iterations in the i-th period
double m_mul
A scalar float32 or float64 Tensor or a Python number. Used to derive the initial learning rate of the i-th period:
double alpha
A scalar float32 or float64 Tensor or a Python number. Minimum learning rate value as a fraction of the learning_rate.
string name
String. Optional name of the operation. Defaults to 'SGDRDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
first_decay_steps = 1000
lr_decayed = cosine_decay_restarts(learning_rate, global_step,
first_decay_steps)

#### objectcosine_decay_restarts_dyn(object learning_rate, object global_step, object first_decay_steps, ImplicitContainer<T> t_mul, ImplicitContainer<T> m_mul, ImplicitContainer<T> alpha, object name)

Applies cosine decay with restarts to the learning rate.

See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies a cosine decay function with restarts to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate while taking into account possible warm restarts. The learning rate multiplier first decays from 1 to alpha for first_decay_steps steps. Then, a warm restart is performed. Each new warm restart runs for t_mul times more steps and with m_mul times smaller initial learning rate.

Example usage:
##### Parameters
object learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
object global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation.
object first_decay_steps
A scalar int32 or int64 Tensor or a Python number. Number of steps to decay over.
ImplicitContainer<T> t_mul
A scalar float32 or float64 Tensor or a Python number. Used to derive the number of iterations in the i-th period
ImplicitContainer<T> m_mul
A scalar float32 or float64 Tensor or a Python number. Used to derive the initial learning rate of the i-th period:
ImplicitContainer<T> alpha
A scalar float32 or float64 Tensor or a Python number. Minimum learning rate value as a fraction of the learning_rate.
object name
String. Optional name of the operation. Defaults to 'SGDRDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
first_decay_steps = 1000
lr_decayed = cosine_decay_restarts(learning_rate, global_step,
first_decay_steps)

#### objectdo_quantize_training_on_graphdef(object input_graph, int num_bits)

A general quantization scheme is being developed in tf.contrib.quantize. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: GraphDef quantized training rewriter is deprecated in the long term

Consider using that instead, though since it is in the tf.contrib namespace, it is not subject to backward compatibility guarantees.

#### objectdo_quantize_training_on_graphdef_dyn(object input_graph, object num_bits)

A general quantization scheme is being developed in tf.contrib.quantize. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: GraphDef quantized training rewriter is deprecated in the long term

Consider using that instead, though since it is in the tf.contrib namespace, it is not subject to backward compatibility guarantees.

#### objectexponential_decay(double learning_rate, IGraphNodeBase global_step, int decay_steps, double decay_rate, bool staircase, string name)

Applies exponential decay to the learning rate.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies an exponential decay function to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: If the argument staircase is True, then global_step / decay_steps is an integer division and the decayed learning rate follows a staircase function.

Example: decay every 100000 steps with a base of 0.96:
##### Parameters
double learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
IGraphNodeBase global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation. Must not be negative.
int decay_steps
A scalar int32 or int64 Tensor or a Python number. Must be positive. See the decay computation above.
double decay_rate
A scalar float32 or float64 Tensor or a Python number. The decay rate.
bool staircase
Boolean. If True decay the learning rate at discrete intervals
string name
String. Optional name of the operation. Defaults to 'ExponentialDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
decayed_learning_rate = learning_rate *
decay_rate ^ (global_step / decay_steps)

#### objectexponential_decay(double learning_rate, int global_step, int decay_steps, double decay_rate, bool staircase, string name)

Applies exponential decay to the learning rate.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies an exponential decay function to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: If the argument staircase is True, then global_step / decay_steps is an integer division and the decayed learning rate follows a staircase function.

Example: decay every 100000 steps with a base of 0.96:
##### Parameters
double learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
int global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation. Must not be negative.
int decay_steps
A scalar int32 or int64 Tensor or a Python number. Must be positive. See the decay computation above.
double decay_rate
A scalar float32 or float64 Tensor or a Python number. The decay rate.
bool staircase
Boolean. If True decay the learning rate at discrete intervals
string name
String. Optional name of the operation. Defaults to 'ExponentialDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
decayed_learning_rate = learning_rate *
decay_rate ^ (global_step / decay_steps)

#### objectexponential_decay(double learning_rate, ResourceVariable global_step, int decay_steps, double decay_rate, bool staircase, string name)

Applies exponential decay to the learning rate.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies an exponential decay function to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: If the argument staircase is True, then global_step / decay_steps is an integer division and the decayed learning rate follows a staircase function.

Example: decay every 100000 steps with a base of 0.96:
##### Parameters
double learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
ResourceVariable global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation. Must not be negative.
int decay_steps
A scalar int32 or int64 Tensor or a Python number. Must be positive. See the decay computation above.
double decay_rate
A scalar float32 or float64 Tensor or a Python number. The decay rate.
bool staircase
Boolean. If True decay the learning rate at discrete intervals
string name
String. Optional name of the operation. Defaults to 'ExponentialDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
decayed_learning_rate = learning_rate *
decay_rate ^ (global_step / decay_steps)

#### objectexponential_decay_dyn(object learning_rate, object global_step, object decay_steps, object decay_rate, ImplicitContainer<T> staircase, object name)

Applies exponential decay to the learning rate.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies an exponential decay function to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: If the argument staircase is True, then global_step / decay_steps is an integer division and the decayed learning rate follows a staircase function.

Example: decay every 100000 steps with a base of 0.96:
##### Parameters
object learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
object global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation. Must not be negative.
object decay_steps
A scalar int32 or int64 Tensor or a Python number. Must be positive. See the decay computation above.
object decay_rate
A scalar float32 or float64 Tensor or a Python number. The decay rate.
ImplicitContainer<T> staircase
Boolean. If True decay the learning rate at discrete intervals
object name
String. Optional name of the operation. Defaults to 'ExponentialDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
decayed_learning_rate = learning_rate *
decay_rate ^ (global_step / decay_steps)

#### objectexport_meta_graph(IEnumerable<object> filename, object meta_info_def, IEnumerable<object> graph_def, Nullable<int> saver_def, IEnumerable<object> collection_list, bool as_text, Graph graph, object export_scope, Nullable<bool> clear_devices, bool clear_extraneous_savers, bool strip_default_attrs, bool save_debug_info, IDictionary<string, object> kwargs)

Returns MetaGraphDef proto.

Optionally writes it to filename.

This function exports the graph, saver, and collection objects into MetaGraphDef protocol buffer with the intention of it being imported at a later time or location to restart training, run inference, or be a subgraph.
##### Parameters
IEnumerable<object> filename
Optional filename including the path for writing the generated MetaGraphDef protocol buffer.
object meta_info_def
MetaInfoDef protocol buffer.
IEnumerable<object> graph_def
GraphDef protocol buffer.
Nullable<int> saver_def
SaverDef protocol buffer.
IEnumerable<object> collection_list
List of string keys to collect.
bool as_text
If True, writes the MetaGraphDef as an ASCII proto.
Graph graph
The Graph to export. If None, use the default graph.
object export_scope
Optional string. Name scope under which to extract the subgraph. The scope name will be striped from the node definitions for easy import later into new name scopes. If None, the whole graph is exported. graph_def and export_scope cannot both be specified.
Nullable<bool> clear_devices
Whether or not to clear the device field for an Operation or Tensor during export.
bool clear_extraneous_savers
Remove any Saver-related information from the graph (both Save/Restore ops and SaverDefs) that are not associated with the provided SaverDef.
bool strip_default_attrs
Boolean. If True, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
bool save_debug_info
If True, save the GraphDebugInfo to a separate file, which in the same directory of filename and with _debug added before the file extend.
IDictionary<string, object> kwargs
Optional keyed arguments.
##### Returns
object
A MetaGraphDef proto.

#### objectexport_meta_graph(IEnumerable<object> filename, object meta_info_def, IEnumerable<object> graph_def, Nullable<int> saver_def, IEnumerable<object> collection_list, bool as_text, Graph graph, object export_scope, Nullable<bool> clear_devices, bool clear_extraneous_savers, Saver strip_default_attrs, bool save_debug_info, IDictionary<string, object> kwargs)

Returns MetaGraphDef proto.

Optionally writes it to filename.

This function exports the graph, saver, and collection objects into MetaGraphDef protocol buffer with the intention of it being imported at a later time or location to restart training, run inference, or be a subgraph.
##### Parameters
IEnumerable<object> filename
Optional filename including the path for writing the generated MetaGraphDef protocol buffer.
object meta_info_def
MetaInfoDef protocol buffer.
IEnumerable<object> graph_def
GraphDef protocol buffer.
Nullable<int> saver_def
SaverDef protocol buffer.
IEnumerable<object> collection_list
List of string keys to collect.
bool as_text
If True, writes the MetaGraphDef as an ASCII proto.
Graph graph
The Graph to export. If None, use the default graph.
object export_scope
Optional string. Name scope under which to extract the subgraph. The scope name will be striped from the node definitions for easy import later into new name scopes. If None, the whole graph is exported. graph_def and export_scope cannot both be specified.
Nullable<bool> clear_devices
Whether or not to clear the device field for an Operation or Tensor during export.
bool clear_extraneous_savers
Remove any Saver-related information from the graph (both Save/Restore ops and SaverDefs) that are not associated with the provided SaverDef.
Saver strip_default_attrs
Boolean. If True, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
bool save_debug_info
If True, save the GraphDebugInfo to a separate file, which in the same directory of filename and with _debug added before the file extend.
IDictionary<string, object> kwargs
Optional keyed arguments.
##### Returns
object
A MetaGraphDef proto.

#### objectexport_meta_graph_dyn(object filename, object meta_info_def, object graph_def, object saver_def, object collection_list, ImplicitContainer<T> as_text, object graph, object export_scope, ImplicitContainer<T> clear_devices, ImplicitContainer<T> clear_extraneous_savers, ImplicitContainer<T> strip_default_attrs, ImplicitContainer<T> save_debug_info, IDictionary<string, object> kwargs)

Returns MetaGraphDef proto.

Optionally writes it to filename.

This function exports the graph, saver, and collection objects into MetaGraphDef protocol buffer with the intention of it being imported at a later time or location to restart training, run inference, or be a subgraph.
##### Parameters
object filename
Optional filename including the path for writing the generated MetaGraphDef protocol buffer.
object meta_info_def
MetaInfoDef protocol buffer.
object graph_def
GraphDef protocol buffer.
object saver_def
SaverDef protocol buffer.
object collection_list
List of string keys to collect.
ImplicitContainer<T> as_text
If True, writes the MetaGraphDef as an ASCII proto.
object graph
The Graph to export. If None, use the default graph.
object export_scope
Optional string. Name scope under which to extract the subgraph. The scope name will be striped from the node definitions for easy import later into new name scopes. If None, the whole graph is exported. graph_def and export_scope cannot both be specified.
ImplicitContainer<T> clear_devices
Whether or not to clear the device field for an Operation or Tensor during export.
ImplicitContainer<T> clear_extraneous_savers
Remove any Saver-related information from the graph (both Save/Restore ops and SaverDefs) that are not associated with the provided SaverDef.
ImplicitContainer<T> strip_default_attrs
Boolean. If True, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
ImplicitContainer<T> save_debug_info
If True, save the GraphDebugInfo to a separate file, which in the same directory of filename and with _debug added before the file extend.
IDictionary<string, object> kwargs
Optional keyed arguments.
##### Returns
object
A MetaGraphDef proto.

#### objectgenerate_checkpoint_state_proto(string save_dir, Byte[] model_checkpoint_path, IEnumerable<object> all_model_checkpoint_paths, object all_model_checkpoint_timestamps, Nullable<double> last_preserved_timestamp)

Generates a checkpoint state proto.
##### Parameters
string save_dir
Directory where the model was saved.
Byte[] model_checkpoint_path
The checkpoint file.
IEnumerable<object> all_model_checkpoint_paths
List of strings. Paths to all not-yet-deleted checkpoints, sorted from oldest to newest. If this is a non-empty list, the last element must be equal to model_checkpoint_path. These paths are also saved in the CheckpointState proto.
object all_model_checkpoint_timestamps
A list of floats, indicating the number of seconds since the Epoch when each checkpoint was generated.
Nullable<double> last_preserved_timestamp
A float, indicating the number of seconds since the Epoch when the last preserved checkpoint was written, e.g. due to a keep_checkpoint_every_n_hours parameter (see tf.contrib.checkpoint.CheckpointManager for an implementation).
##### Returns
object
CheckpointState proto with model_checkpoint_path and all_model_checkpoint_paths updated to either absolute paths or relative paths to the current save_dir.

#### objectgenerate_checkpoint_state_proto(string save_dir, IEnumerable<object> model_checkpoint_path, IEnumerable<object> all_model_checkpoint_paths, object all_model_checkpoint_timestamps, Nullable<double> last_preserved_timestamp)

Generates a checkpoint state proto.
##### Parameters
string save_dir
Directory where the model was saved.
IEnumerable<object> model_checkpoint_path
The checkpoint file.
IEnumerable<object> all_model_checkpoint_paths
List of strings. Paths to all not-yet-deleted checkpoints, sorted from oldest to newest. If this is a non-empty list, the last element must be equal to model_checkpoint_path. These paths are also saved in the CheckpointState proto.
object all_model_checkpoint_timestamps
A list of floats, indicating the number of seconds since the Epoch when each checkpoint was generated.
Nullable<double> last_preserved_timestamp
A float, indicating the number of seconds since the Epoch when the last preserved checkpoint was written, e.g. due to a keep_checkpoint_every_n_hours parameter (see tf.contrib.checkpoint.CheckpointManager for an implementation).
##### Returns
object
CheckpointState proto with model_checkpoint_path and all_model_checkpoint_paths updated to either absolute paths or relative paths to the current save_dir.

#### objectgenerate_checkpoint_state_proto(string save_dir, IGraphNodeBase model_checkpoint_path, IEnumerable<object> all_model_checkpoint_paths, object all_model_checkpoint_timestamps, Nullable<double> last_preserved_timestamp)

Generates a checkpoint state proto.
##### Parameters
string save_dir
Directory where the model was saved.
IGraphNodeBase model_checkpoint_path
The checkpoint file.
IEnumerable<object> all_model_checkpoint_paths
List of strings. Paths to all not-yet-deleted checkpoints, sorted from oldest to newest. If this is a non-empty list, the last element must be equal to model_checkpoint_path. These paths are also saved in the CheckpointState proto.
object all_model_checkpoint_timestamps
A list of floats, indicating the number of seconds since the Epoch when each checkpoint was generated.
Nullable<double> last_preserved_timestamp
A float, indicating the number of seconds since the Epoch when the last preserved checkpoint was written, e.g. due to a keep_checkpoint_every_n_hours parameter (see tf.contrib.checkpoint.CheckpointManager for an implementation).
##### Returns
object
CheckpointState proto with model_checkpoint_path and all_model_checkpoint_paths updated to either absolute paths or relative paths to the current save_dir.

#### objectgenerate_checkpoint_state_proto(string save_dir, string model_checkpoint_path, IEnumerable<object> all_model_checkpoint_paths, object all_model_checkpoint_timestamps, Nullable<double> last_preserved_timestamp)

Generates a checkpoint state proto.
##### Parameters
string save_dir
Directory where the model was saved.
string model_checkpoint_path
The checkpoint file.
IEnumerable<object> all_model_checkpoint_paths
List of strings. Paths to all not-yet-deleted checkpoints, sorted from oldest to newest. If this is a non-empty list, the last element must be equal to model_checkpoint_path. These paths are also saved in the CheckpointState proto.
object all_model_checkpoint_timestamps
A list of floats, indicating the number of seconds since the Epoch when each checkpoint was generated.
Nullable<double> last_preserved_timestamp
A float, indicating the number of seconds since the Epoch when the last preserved checkpoint was written, e.g. due to a keep_checkpoint_every_n_hours parameter (see tf.contrib.checkpoint.CheckpointManager for an implementation).
##### Returns
object
CheckpointState proto with model_checkpoint_path and all_model_checkpoint_paths updated to either absolute paths or relative paths to the current save_dir.

#### objectgenerate_checkpoint_state_proto_dyn(object save_dir, object model_checkpoint_path, object all_model_checkpoint_paths, object all_model_checkpoint_timestamps, object last_preserved_timestamp)

Generates a checkpoint state proto.
##### Parameters
object save_dir
Directory where the model was saved.
object model_checkpoint_path
The checkpoint file.
object all_model_checkpoint_paths
List of strings. Paths to all not-yet-deleted checkpoints, sorted from oldest to newest. If this is a non-empty list, the last element must be equal to model_checkpoint_path. These paths are also saved in the CheckpointState proto.
object all_model_checkpoint_timestamps
A list of floats, indicating the number of seconds since the Epoch when each checkpoint was generated.
object last_preserved_timestamp
A float, indicating the number of seconds since the Epoch when the last preserved checkpoint was written, e.g. due to a keep_checkpoint_every_n_hours parameter (see tf.contrib.checkpoint.CheckpointManager for an implementation).
##### Returns
object
CheckpointState proto with model_checkpoint_path and all_model_checkpoint_paths updated to either absolute paths or relative paths to the current save_dir.

#### IList<object>get_checkpoint_mtimes(IEnumerable<object> checkpoint_prefixes)

Returns the mtimes (modification timestamps) of the checkpoints. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use standard file utilities to get mtimes.

Globs for the checkpoints pointed to by checkpoint_prefixes. If the files exist, collect their mtime. Both V2 and V1 checkpoints are considered, in that priority.

This is the recommended way to get the mtimes, since it takes into account the naming difference between V1 and V2 formats.

Note: If not all checkpoints exist, the length of the returned mtimes list will be smaller than the length of checkpoint_prefixes list, so mapping checkpoints to corresponding mtimes will not be possible.
##### Parameters
IEnumerable<object> checkpoint_prefixes
a list of checkpoint paths, typically the results of Saver.save() or those of tf.train.latest_checkpoint(), regardless of sharded/non-sharded or V1/V2.
##### Returns
IList<object>
A list of mtimes (in microseconds) of the found checkpoints.

#### objectget_checkpoint_mtimes_dyn(object checkpoint_prefixes)

Returns the mtimes (modification timestamps) of the checkpoints. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use standard file utilities to get mtimes.

Globs for the checkpoints pointed to by checkpoint_prefixes. If the files exist, collect their mtime. Both V2 and V1 checkpoints are considered, in that priority.

This is the recommended way to get the mtimes, since it takes into account the naming difference between V1 and V2 formats.

Note: If not all checkpoints exist, the length of the returned mtimes list will be smaller than the length of checkpoint_prefixes list, so mapping checkpoints to corresponding mtimes will not be possible.
##### Parameters
object checkpoint_prefixes
a list of checkpoint paths, typically the results of Saver.save() or those of tf.train.latest_checkpoint(), regardless of sharded/non-sharded or V1/V2.
##### Returns
object
A list of mtimes (in microseconds) of the found checkpoints.

#### objectget_checkpoint_state(Byte[] checkpoint_dir, string latest_filename)

Returns CheckpointState proto from the "checkpoint" file.

If the "checkpoint" file contains a valid CheckpointState proto, returns it.
##### Parameters
Byte[] checkpoint_dir
The directory of checkpoints.
string latest_filename
Optional name of the checkpoint file. Default to 'checkpoint'.
##### Returns
object
A CheckpointState if the state was available, None otherwise.

#### objectget_checkpoint_state(string checkpoint_dir, string latest_filename)

Returns CheckpointState proto from the "checkpoint" file.

If the "checkpoint" file contains a valid CheckpointState proto, returns it.
##### Parameters
string checkpoint_dir
The directory of checkpoints.
string latest_filename
Optional name of the checkpoint file. Default to 'checkpoint'.
##### Returns
object
A CheckpointState if the state was available, None otherwise.

#### objectget_checkpoint_state_dyn(object checkpoint_dir, object latest_filename)

Returns CheckpointState proto from the "checkpoint" file.

If the "checkpoint" file contains a valid CheckpointState proto, returns it.
##### Parameters
object checkpoint_dir
The directory of checkpoints.
object latest_filename
Optional name of the checkpoint file. Default to 'checkpoint'.
##### Returns
object
A CheckpointState if the state was available, None otherwise.

#### objectget_global_step(Graph graph)

Get the global step tensor.

The global step tensor must be an integer variable. We first try to find it in the collection GLOBAL_STEP, or by name global_step:0.
##### Parameters
Graph graph
The graph to find the global step in. If missing, use default graph.
##### Returns
object
The global step variable, or None if none was found.

#### intglobal_step(_WrappedSession sess, object global_step_tensor)

Small helper to get the global step.
##### Parameters
_WrappedSession sess
A TensorFlow Session object.
object global_step_tensor
Tensor or the name of the operation that contains the global step.
##### Returns
int
The global step value.
Show Example
# Create a variable to hold the global_step.
global_step_tensor = tf.Variable(10, trainable=False, name='global_step')
# Create a session.
sess = tf.compat.v1.Session()
# Initialize the variable
sess.run(global_step_tensor.initializer)
# Get the variable value.
print('global_step: %s' % tf.compat.v1.train.global_step(sess,
global_step_tensor))

global_step: 10

#### intglobal_step(_WrappedSession sess, IEnumerable<object> global_step_tensor)

Small helper to get the global step.
##### Parameters
_WrappedSession sess
A TensorFlow Session object.
IEnumerable<object> global_step_tensor
Tensor or the name of the operation that contains the global step.
##### Returns
int
The global step value.
Show Example
# Create a variable to hold the global_step.
global_step_tensor = tf.Variable(10, trainable=False, name='global_step')
# Create a session.
sess = tf.compat.v1.Session()
# Initialize the variable
sess.run(global_step_tensor.initializer)
# Get the variable value.
print('global_step: %s' % tf.compat.v1.train.global_step(sess,
global_step_tensor))

global_step: 10

#### intglobal_step(BaseSession sess, object global_step_tensor)

Small helper to get the global step.
##### Parameters
BaseSession sess
A TensorFlow Session object.
object global_step_tensor
Tensor or the name of the operation that contains the global step.
##### Returns
int
The global step value.
Show Example
# Create a variable to hold the global_step.
global_step_tensor = tf.Variable(10, trainable=False, name='global_step')
# Create a session.
sess = tf.compat.v1.Session()
# Initialize the variable
sess.run(global_step_tensor.initializer)
# Get the variable value.
print('global_step: %s' % tf.compat.v1.train.global_step(sess,
global_step_tensor))

global_step: 10

#### intglobal_step(BaseSession sess, IEnumerable<object> global_step_tensor)

Small helper to get the global step.
##### Parameters
BaseSession sess
A TensorFlow Session object.
IEnumerable<object> global_step_tensor
Tensor or the name of the operation that contains the global step.
##### Returns
int
The global step value.
Show Example
# Create a variable to hold the global_step.
global_step_tensor = tf.Variable(10, trainable=False, name='global_step')
# Create a session.
sess = tf.compat.v1.Session()
# Initialize the variable
sess.run(global_step_tensor.initializer)
# Get the variable value.
print('global_step: %s' % tf.compat.v1.train.global_step(sess,
global_step_tensor))

global_step: 10

#### objectglobal_step_dyn(object sess, object global_step_tensor)

Small helper to get the global step.
##### Parameters
object sess
A TensorFlow Session object.
object global_step_tensor
Tensor or the name of the operation that contains the global step.
##### Returns
object
The global step value.
Show Example
# Create a variable to hold the global_step.
global_step_tensor = tf.Variable(10, trainable=False, name='global_step')
# Create a session.
sess = tf.compat.v1.Session()
# Initialize the variable
sess.run(global_step_tensor.initializer)
# Get the variable value.
print('global_step: %s' % tf.compat.v1.train.global_step(sess,
global_step_tensor))

global_step: 10

#### Saverimport_meta_graph(int meta_graph_or_file, bool clear_devices, string import_scope, IDictionary<string, object> kwargs)

Recreates a Graph saved in a MetaGraphDef proto.

This function takes a MetaGraphDef protocol buffer as input. If the argument is a file containing a MetaGraphDef protocol buffer , it constructs a protocol buffer from the file content. The function then adds all the nodes from the graph_def field to the current graph, recreates all the collections, and returns a saver constructed from the saver_def field.

In combination with export_meta_graph(), this function can be used to

* Serialize a graph along with other Python objects such as QueueRunner, Variable into a MetaGraphDef.

* Restart training from a saved graph and checkpoints.

* Run inference from a saved graph and checkpoints. Later we can continue training from this saved meta_graph without building the model from scratch. NOTE: Restarting training from saved meta_graph only works if the device assignments have not changed.

Example: Variables, placeholders, and independent operations can also be stored, as shown in the following example. Later this model can be restored and contents loaded.
##### Parameters
int meta_graph_or_file
MetaGraphDef protocol buffer or filename (including the path) containing a MetaGraphDef.
bool clear_devices
Whether or not to clear the device field for an Operation or Tensor during import.
string import_scope
Optional string. Name scope to add. Only used when initializing from protocol buffer.
IDictionary<string, object> kwargs
Optional keyed arguments.
##### Returns
Saver
A saver constructed from saver_def in MetaGraphDef or None.

A None value is returned if no variables exist in the MetaGraphDef (i.e., there are no variables to restore).
Show Example
...
# Create a saver.
saver = tf.compat.v1.train.Saver(...variables...)
# Remember the training_op we want to run by adding it to a collection.
sess = tf.compat.v1.Session()
for step in xrange(1000000):
sess.run(train_op)
if step % 1000 == 0:
# Saves checkpoint, which by default also exports a meta_graph
# named 'my-model-global_step.meta'.
saver.save(sess, 'my-model', global_step=step)

#### Saverimport_meta_graph(IEnumerable<string> meta_graph_or_file, bool clear_devices, string import_scope, IDictionary<string, object> kwargs)

Recreates a Graph saved in a MetaGraphDef proto.

This function takes a MetaGraphDef protocol buffer as input. If the argument is a file containing a MetaGraphDef protocol buffer , it constructs a protocol buffer from the file content. The function then adds all the nodes from the graph_def field to the current graph, recreates all the collections, and returns a saver constructed from the saver_def field.

In combination with export_meta_graph(), this function can be used to

* Serialize a graph along with other Python objects such as QueueRunner, Variable into a MetaGraphDef.

* Restart training from a saved graph and checkpoints.

* Run inference from a saved graph and checkpoints. Later we can continue training from this saved meta_graph without building the model from scratch. NOTE: Restarting training from saved meta_graph only works if the device assignments have not changed.

Example: Variables, placeholders, and independent operations can also be stored, as shown in the following example. Later this model can be restored and contents loaded.
##### Parameters
IEnumerable<string> meta_graph_or_file
MetaGraphDef protocol buffer or filename (including the path) containing a MetaGraphDef.
bool clear_devices
Whether or not to clear the device field for an Operation or Tensor during import.
string import_scope
Optional string. Name scope to add. Only used when initializing from protocol buffer.
IDictionary<string, object> kwargs
Optional keyed arguments.
##### Returns
Saver
A saver constructed from saver_def in MetaGraphDef or None.

A None value is returned if no variables exist in the MetaGraphDef (i.e., there are no variables to restore).
Show Example
...
# Create a saver.
saver = tf.compat.v1.train.Saver(...variables...)
# Remember the training_op we want to run by adding it to a collection.
sess = tf.compat.v1.Session()
for step in xrange(1000000):
sess.run(train_op)
if step % 1000 == 0:
# Saves checkpoint, which by default also exports a meta_graph
# named 'my-model-global_step.meta'.
saver.save(sess, 'my-model', global_step=step)

#### Saverimport_meta_graph(string meta_graph_or_file, bool clear_devices, string import_scope, IDictionary<string, object> kwargs)

Recreates a Graph saved in a MetaGraphDef proto.

This function takes a MetaGraphDef protocol buffer as input. If the argument is a file containing a MetaGraphDef protocol buffer , it constructs a protocol buffer from the file content. The function then adds all the nodes from the graph_def field to the current graph, recreates all the collections, and returns a saver constructed from the saver_def field.

In combination with export_meta_graph(), this function can be used to

* Serialize a graph along with other Python objects such as QueueRunner, Variable into a MetaGraphDef.

* Restart training from a saved graph and checkpoints.

* Run inference from a saved graph and checkpoints. Later we can continue training from this saved meta_graph without building the model from scratch. NOTE: Restarting training from saved meta_graph only works if the device assignments have not changed.

Example: Variables, placeholders, and independent operations can also be stored, as shown in the following example. Later this model can be restored and contents loaded.
##### Parameters
string meta_graph_or_file
MetaGraphDef protocol buffer or filename (including the path) containing a MetaGraphDef.
bool clear_devices
Whether or not to clear the device field for an Operation or Tensor during import.
string import_scope
Optional string. Name scope to add. Only used when initializing from protocol buffer.
IDictionary<string, object> kwargs
Optional keyed arguments.
##### Returns
Saver
A saver constructed from saver_def in MetaGraphDef or None.

A None value is returned if no variables exist in the MetaGraphDef (i.e., there are no variables to restore).
Show Example
...
# Create a saver.
saver = tf.compat.v1.train.Saver(...variables...)
# Remember the training_op we want to run by adding it to a collection.
sess = tf.compat.v1.Session()
for step in xrange(1000000):
sess.run(train_op)
if step % 1000 == 0:
# Saves checkpoint, which by default also exports a meta_graph
# named 'my-model-global_step.meta'.
saver.save(sess, 'my-model', global_step=step)

#### objectimport_meta_graph_dyn(object meta_graph_or_file, ImplicitContainer<T> clear_devices, object import_scope, IDictionary<string, object> kwargs)

Recreates a Graph saved in a MetaGraphDef proto.

This function takes a MetaGraphDef protocol buffer as input. If the argument is a file containing a MetaGraphDef protocol buffer , it constructs a protocol buffer from the file content. The function then adds all the nodes from the graph_def field to the current graph, recreates all the collections, and returns a saver constructed from the saver_def field.

In combination with export_meta_graph(), this function can be used to

* Serialize a graph along with other Python objects such as QueueRunner, Variable into a MetaGraphDef.

* Restart training from a saved graph and checkpoints.

* Run inference from a saved graph and checkpoints. Later we can continue training from this saved meta_graph without building the model from scratch. NOTE: Restarting training from saved meta_graph only works if the device assignments have not changed.

Example: Variables, placeholders, and independent operations can also be stored, as shown in the following example. Later this model can be restored and contents loaded.
##### Parameters
object meta_graph_or_file
MetaGraphDef protocol buffer or filename (including the path) containing a MetaGraphDef.
ImplicitContainer<T> clear_devices
Whether or not to clear the device field for an Operation or Tensor during import.
object import_scope
Optional string. Name scope to add. Only used when initializing from protocol buffer.
IDictionary<string, object> kwargs
Optional keyed arguments.
##### Returns
object
A saver constructed from saver_def in MetaGraphDef or None.

A None value is returned if no variables exist in the MetaGraphDef (i.e., there are no variables to restore).
Show Example
...
# Create a saver.
saver = tf.compat.v1.train.Saver(...variables...)
# Remember the training_op we want to run by adding it to a collection.
sess = tf.compat.v1.Session()
for step in xrange(1000000):
sess.run(train_op)
if step % 1000 == 0:
# Saves checkpoint, which by default also exports a meta_graph
# named 'my-model-global_step.meta'.
saver.save(sess, 'my-model', global_step=step)

#### objectinput_producer(IEnumerable<object> input_tensor, IEnumerable<object> element_shape, Nullable<int> num_epochs, bool shuffle, Nullable<int> seed, int capacity, string shared_name, string summary_name, PythonFunctionContainer name, object cancel_op)

Output the rows of input_tensor to a queue for an input pipeline. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Note: if num_epochs is not None, this function creates local counter epochs. Use local_variables_initializer() to initialize local variables.
##### Parameters
IEnumerable<object> input_tensor
A tensor with the rows to produce. Must be at least one-dimensional. Must either have a fully-defined shape, or element_shape must be defined.
IEnumerable<object> element_shape
(Optional.) A TensorShape representing the shape of a row of input_tensor, if it cannot be inferred.
Nullable<int> num_epochs
(Optional.) An integer. If specified input_producer produces each row of input_tensor num_epochs times before generating an OutOfRange error. If not specified, input_producer can cycle through the rows of input_tensor an unlimited number of times.
bool shuffle
(Optional.) A boolean. If true, the rows are randomly shuffled within each epoch.
Nullable<int> seed
(Optional.) An integer. The seed to use if shuffle is true.
int capacity
(Optional.) The capacity of the queue to be used for buffering the input.
string shared_name
(Optional.) If set, this queue will be shared under the given name across multiple sessions.
string summary_name
(Optional.) If set, a scalar summary for the current queue size will be generated, using this name as part of the tag.
PythonFunctionContainer name
(Optional.) A name for queue.
object cancel_op
(Optional.) Cancel op for the queue
##### Returns
object
A queue with the output rows. A QueueRunner for the queue is added to the current QUEUE_RUNNER collection of the current graph.

#### objectinput_producer(IEnumerable<object> input_tensor, IEnumerable<object> element_shape, Nullable<int> num_epochs, bool shuffle, Nullable<int> seed, int capacity, string shared_name, string summary_name, string name, object cancel_op)

Output the rows of input_tensor to a queue for an input pipeline. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Note: if num_epochs is not None, this function creates local counter epochs. Use local_variables_initializer() to initialize local variables.
##### Parameters
IEnumerable<object> input_tensor
A tensor with the rows to produce. Must be at least one-dimensional. Must either have a fully-defined shape, or element_shape must be defined.
IEnumerable<object> element_shape
(Optional.) A TensorShape representing the shape of a row of input_tensor, if it cannot be inferred.
Nullable<int> num_epochs
(Optional.) An integer. If specified input_producer produces each row of input_tensor num_epochs times before generating an OutOfRange error. If not specified, input_producer can cycle through the rows of input_tensor an unlimited number of times.
bool shuffle
(Optional.) A boolean. If true, the rows are randomly shuffled within each epoch.
Nullable<int> seed
(Optional.) An integer. The seed to use if shuffle is true.
int capacity
(Optional.) The capacity of the queue to be used for buffering the input.
string shared_name
(Optional.) If set, this queue will be shared under the given name across multiple sessions.
string summary_name
(Optional.) If set, a scalar summary for the current queue size will be generated, using this name as part of the tag.
string name
(Optional.) A name for queue.
object cancel_op
(Optional.) Cancel op for the queue
##### Returns
object
A queue with the output rows. A QueueRunner for the queue is added to the current QUEUE_RUNNER collection of the current graph.

#### objectinput_producer(IGraphNodeBase input_tensor, IEnumerable<object> element_shape, Nullable<int> num_epochs, bool shuffle, Nullable<int> seed, int capacity, string shared_name, string summary_name, PythonFunctionContainer name, object cancel_op)

Output the rows of input_tensor to a queue for an input pipeline. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Note: if num_epochs is not None, this function creates local counter epochs. Use local_variables_initializer() to initialize local variables.
##### Parameters
IGraphNodeBase input_tensor
A tensor with the rows to produce. Must be at least one-dimensional. Must either have a fully-defined shape, or element_shape must be defined.
IEnumerable<object> element_shape
(Optional.) A TensorShape representing the shape of a row of input_tensor, if it cannot be inferred.
Nullable<int> num_epochs
(Optional.) An integer. If specified input_producer produces each row of input_tensor num_epochs times before generating an OutOfRange error. If not specified, input_producer can cycle through the rows of input_tensor an unlimited number of times.
bool shuffle
(Optional.) A boolean. If true, the rows are randomly shuffled within each epoch.
Nullable<int> seed
(Optional.) An integer. The seed to use if shuffle is true.
int capacity
(Optional.) The capacity of the queue to be used for buffering the input.
string shared_name
(Optional.) If set, this queue will be shared under the given name across multiple sessions.
string summary_name
(Optional.) If set, a scalar summary for the current queue size will be generated, using this name as part of the tag.
PythonFunctionContainer name
(Optional.) A name for queue.
object cancel_op
(Optional.) Cancel op for the queue
##### Returns
object
A queue with the output rows. A QueueRunner for the queue is added to the current QUEUE_RUNNER collection of the current graph.

#### objectinput_producer(IGraphNodeBase input_tensor, IEnumerable<object> element_shape, Nullable<int> num_epochs, bool shuffle, Nullable<int> seed, int capacity, string shared_name, string summary_name, string name, object cancel_op)

Output the rows of input_tensor to a queue for an input pipeline. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Note: if num_epochs is not None, this function creates local counter epochs. Use local_variables_initializer() to initialize local variables.
##### Parameters
IGraphNodeBase input_tensor
A tensor with the rows to produce. Must be at least one-dimensional. Must either have a fully-defined shape, or element_shape must be defined.
IEnumerable<object> element_shape
(Optional.) A TensorShape representing the shape of a row of input_tensor, if it cannot be inferred.
Nullable<int> num_epochs
(Optional.) An integer. If specified input_producer produces each row of input_tensor num_epochs times before generating an OutOfRange error. If not specified, input_producer can cycle through the rows of input_tensor an unlimited number of times.
bool shuffle
(Optional.) A boolean. If true, the rows are randomly shuffled within each epoch.
Nullable<int> seed
(Optional.) An integer. The seed to use if shuffle is true.
int capacity
(Optional.) The capacity of the queue to be used for buffering the input.
string shared_name
(Optional.) If set, this queue will be shared under the given name across multiple sessions.
string summary_name
(Optional.) If set, a scalar summary for the current queue size will be generated, using this name as part of the tag.
string name
(Optional.) A name for queue.
object cancel_op
(Optional.) Cancel op for the queue
##### Returns
object
A queue with the output rows. A QueueRunner for the queue is added to the current QUEUE_RUNNER collection of the current graph.

#### objectinput_producer_dyn(object input_tensor, object element_shape, object num_epochs, ImplicitContainer<T> shuffle, object seed, ImplicitContainer<T> capacity, object shared_name, object summary_name, object name, object cancel_op)

Output the rows of input_tensor to a queue for an input pipeline. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Note: if num_epochs is not None, this function creates local counter epochs. Use local_variables_initializer() to initialize local variables.
##### Parameters
object input_tensor
A tensor with the rows to produce. Must be at least one-dimensional. Must either have a fully-defined shape, or element_shape must be defined.
object element_shape
(Optional.) A TensorShape representing the shape of a row of input_tensor, if it cannot be inferred.
object num_epochs
(Optional.) An integer. If specified input_producer produces each row of input_tensor num_epochs times before generating an OutOfRange error. If not specified, input_producer can cycle through the rows of input_tensor an unlimited number of times.
ImplicitContainer<T> shuffle
(Optional.) A boolean. If true, the rows are randomly shuffled within each epoch.
object seed
(Optional.) An integer. The seed to use if shuffle is true.
ImplicitContainer<T> capacity
(Optional.) The capacity of the queue to be used for buffering the input.
object shared_name
(Optional.) If set, this queue will be shared under the given name across multiple sessions.
object summary_name
(Optional.) If set, a scalar summary for the current queue size will be generated, using this name as part of the tag.
object name
(Optional.) A name for queue.
object cancel_op
(Optional.) Cancel op for the queue
##### Returns
object
A queue with the output rows. A QueueRunner for the queue is added to the current QUEUE_RUNNER collection of the current graph.

#### objectinverse_time_decay(double learning_rate, ResourceVariable global_step, int decay_steps, double decay_rate, bool staircase, string name)

Applies inverse time decay to the initial learning rate.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies an inverse decay function to a provided initial learning rate. It requires an global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: or, if staircase is True, as: Example: decay 1/t with a rate of 0.5:
##### Parameters
double learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
ResourceVariable global_step
A Python number. Global step to use for the decay computation. Must not be negative.
int decay_steps
How often to apply decay.
double decay_rate
A Python number. The decay rate.
bool staircase
Whether to apply decay in a discrete staircase, as opposed to continuous, fashion.
string name
String. Optional name of the operation. Defaults to 'InverseTimeDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
decayed_learning_rate = learning_rate / (1 + decay_rate * global_step /
decay_step)

#### objectinverse_time_decay_dyn(object learning_rate, object global_step, object decay_steps, object decay_rate, ImplicitContainer<T> staircase, object name)

Applies inverse time decay to the initial learning rate.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies an inverse decay function to a provided initial learning rate. It requires an global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: or, if staircase is True, as: Example: decay 1/t with a rate of 0.5:
##### Parameters
object learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
object global_step
A Python number. Global step to use for the decay computation. Must not be negative.
object decay_steps
How often to apply decay.
object decay_rate
A Python number. The decay rate.
ImplicitContainer<T> staircase
Whether to apply decay in a discrete staircase, as opposed to continuous, fashion.
object name
String. Optional name of the operation. Defaults to 'InverseTimeDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
decayed_learning_rate = learning_rate / (1 + decay_rate * global_step /
decay_step)

#### objectlatest_checkpoint(string checkpoint_dir, string latest_filename)

Finds the filename of latest saved checkpoint file.
##### Parameters
string checkpoint_dir
Directory where the variables were saved.
string latest_filename
Optional name for the protocol buffer file that contains the list of most recent checkpoint filenames. See the corresponding argument to Saver.save().
##### Returns
object
The full path to the latest checkpoint or None if no checkpoint was found.

#### objectlatest_checkpoint(Byte[] checkpoint_dir, string latest_filename)

Finds the filename of latest saved checkpoint file.
##### Parameters
Byte[] checkpoint_dir
Directory where the variables were saved.
string latest_filename
Optional name for the protocol buffer file that contains the list of most recent checkpoint filenames. See the corresponding argument to Saver.save().
##### Returns
object
The full path to the latest checkpoint or None if no checkpoint was found.

#### objectlatest_checkpoint_dyn(object checkpoint_dir, object latest_filename)

Finds the filename of latest saved checkpoint file.
##### Parameters
object checkpoint_dir
Directory where the variables were saved.
object latest_filename
Optional name for the protocol buffer file that contains the list of most recent checkpoint filenames. See the corresponding argument to Saver.save().
##### Returns
object
The full path to the latest checkpoint or None if no checkpoint was found.

#### objectlimit_epochs(IGraphNodeBase tensor, Nullable<int> num_epochs, string name)

Returns tensor num_epochs times and then raises an OutOfRange error. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensors(tensor).repeat(num_epochs).

Note: creates local counter epochs. Use local_variables_initializer() to initialize local variables.
##### Parameters
IGraphNodeBase tensor
Any Tensor.
Nullable<int> num_epochs
A positive integer (optional). If specified, limits the number of steps the output tensor may be evaluated.
string name
A name for the operations (optional).
##### Returns
object
tensor or OutOfRange.

#### objectlimit_epochs(IEnumerable<object> tensor, Nullable<int> num_epochs, string name)

Returns tensor num_epochs times and then raises an OutOfRange error. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensors(tensor).repeat(num_epochs).

Note: creates local counter epochs. Use local_variables_initializer() to initialize local variables.
##### Parameters
IEnumerable<object> tensor
Any Tensor.
Nullable<int> num_epochs
A positive integer (optional). If specified, limits the number of steps the output tensor may be evaluated.
string name
A name for the operations (optional).
##### Returns
object
tensor or OutOfRange.

#### objectlimit_epochs_dyn(object tensor, object num_epochs, object name)

Returns tensor num_epochs times and then raises an OutOfRange error. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensors(tensor).repeat(num_epochs).

Note: creates local counter epochs. Use local_variables_initializer() to initialize local variables.
##### Parameters
object tensor
Any Tensor.
object num_epochs
A positive integer (optional). If specified, limits the number of steps the output tensor may be evaluated.
object name
A name for the operations (optional).
##### Returns
object
tensor or OutOfRange.

#### objectlinear_cosine_decay(double learning_rate, int global_step, int decay_steps, double num_periods, double alpha, double beta, string name)

Applies linear cosine decay to the learning rate.

See [Bello et al., ICML2017] Neural Optimizer Search with RL. https://arxiv.org/abs/1709.07417

For the idea of warm starts here controlled by num_periods, see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

Note that linear cosine decay is more aggressive than cosine decay and larger initial learning rates can typically be used.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies a linear cosine decay function to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: Example usage:
##### Parameters
double learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
int global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation.
int decay_steps
A scalar int32 or int64 Tensor or a Python number. Number of steps to decay over.
double num_periods
Number of periods in the cosine part of the decay. See computation above.
double alpha
See computation above.
double beta
See computation above.
string name
String. Optional name of the operation. Defaults to 'LinearCosineDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
global_step = min(global_step, decay_steps)
linear_decay = (decay_steps - global_step) / decay_steps)
cosine_decay = 0.5 * (
1 + cos(pi * 2 * num_periods * global_step / decay_steps))
decayed = (alpha + linear_decay) * cosine_decay + beta
decayed_learning_rate = learning_rate * decayed

#### objectlinear_cosine_decay(double learning_rate, int global_step, int decay_steps, int num_periods, double alpha, double beta, string name)

Applies linear cosine decay to the learning rate.

See [Bello et al., ICML2017] Neural Optimizer Search with RL. https://arxiv.org/abs/1709.07417

For the idea of warm starts here controlled by num_periods, see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

Note that linear cosine decay is more aggressive than cosine decay and larger initial learning rates can typically be used.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies a linear cosine decay function to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: Example usage:
##### Parameters
double learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
int global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation.
int decay_steps
A scalar int32 or int64 Tensor or a Python number. Number of steps to decay over.
int num_periods
Number of periods in the cosine part of the decay. See computation above.
double alpha
See computation above.
double beta
See computation above.
string name
String. Optional name of the operation. Defaults to 'LinearCosineDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
global_step = min(global_step, decay_steps)
linear_decay = (decay_steps - global_step) / decay_steps)
cosine_decay = 0.5 * (
1 + cos(pi * 2 * num_periods * global_step / decay_steps))
decayed = (alpha + linear_decay) * cosine_decay + beta
decayed_learning_rate = learning_rate * decayed

#### objectlinear_cosine_decay_dyn(object learning_rate, object global_step, object decay_steps, ImplicitContainer<T> num_periods, ImplicitContainer<T> alpha, ImplicitContainer<T> beta, object name)

Applies linear cosine decay to the learning rate.

See [Bello et al., ICML2017] Neural Optimizer Search with RL. https://arxiv.org/abs/1709.07417

For the idea of warm starts here controlled by num_periods, see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

Note that linear cosine decay is more aggressive than cosine decay and larger initial learning rates can typically be used.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies a linear cosine decay function to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: Example usage:
##### Parameters
object learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
object global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation.
object decay_steps
A scalar int32 or int64 Tensor or a Python number. Number of steps to decay over.
ImplicitContainer<T> num_periods
Number of periods in the cosine part of the decay. See computation above.
ImplicitContainer<T> alpha
See computation above.
ImplicitContainer<T> beta
See computation above.
object name
String. Optional name of the operation. Defaults to 'LinearCosineDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
global_step = min(global_step, decay_steps)
linear_decay = (decay_steps - global_step) / decay_steps)
cosine_decay = 0.5 * (
1 + cos(pi * 2 * num_periods * global_step / decay_steps))
decayed = (alpha + linear_decay) * cosine_decay + beta
decayed_learning_rate = learning_rate * decayed

#### IList<ValueTuple<object, object>>list_variables(Byte[] ckpt_dir_or_file)

Returns list of all variables in the checkpoint.
##### Parameters
Byte[] ckpt_dir_or_file
Directory with checkpoints file or path to checkpoint.
##### Returns
IList<ValueTuple<object, object>>
List of tuples (name, shape).

#### objectmaybe_batch(IEnumerable<object> tensors, IGraphNodeBase keep_input, int batch_size, int num_threads, int capacity, bool enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, object shared_name, string name)

Conditionally creates batches of tensors based on keep_input. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

See docstring in batch for more details.
##### Parameters
IEnumerable<object> tensors
The list or dictionary of tensors to enqueue.
IGraphNodeBase keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
int batch_size
The new batch size pulled from the queue.
The number of threads enqueuing tensors. The batching will be nondeterministic if num_threads > 1.
int capacity
An integer. The maximum number of elements in the queue.
bool enqueue_many
Whether each tensor in tensors is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same types as tensors.

#### objectmaybe_batch(IDictionary<object, object> tensors, bool keep_input, int batch_size, int num_threads, int capacity, bool enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, object shared_name, string name)

Conditionally creates batches of tensors based on keep_input. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

See docstring in batch for more details.
##### Parameters
IDictionary<object, object> tensors
The list or dictionary of tensors to enqueue.
bool keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
int batch_size
The new batch size pulled from the queue.
The number of threads enqueuing tensors. The batching will be nondeterministic if num_threads > 1.
int capacity
An integer. The maximum number of elements in the queue.
bool enqueue_many
Whether each tensor in tensors is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same types as tensors.

#### objectmaybe_batch(IEnumerable<object> tensors, bool keep_input, int batch_size, int num_threads, int capacity, bool enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, object shared_name, string name)

Conditionally creates batches of tensors based on keep_input. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

See docstring in batch for more details.
##### Parameters
IEnumerable<object> tensors
The list or dictionary of tensors to enqueue.
bool keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
int batch_size
The new batch size pulled from the queue.
The number of threads enqueuing tensors. The batching will be nondeterministic if num_threads > 1.
int capacity
An integer. The maximum number of elements in the queue.
bool enqueue_many
Whether each tensor in tensors is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same types as tensors.

#### objectmaybe_batch(IDictionary<object, object> tensors, IGraphNodeBase keep_input, int batch_size, int num_threads, int capacity, bool enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, object shared_name, string name)

Conditionally creates batches of tensors based on keep_input. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

See docstring in batch for more details.
##### Parameters
IDictionary<object, object> tensors
The list or dictionary of tensors to enqueue.
IGraphNodeBase keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
int batch_size
The new batch size pulled from the queue.
The number of threads enqueuing tensors. The batching will be nondeterministic if num_threads > 1.
int capacity
An integer. The maximum number of elements in the queue.
bool enqueue_many
Whether each tensor in tensors is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same types as tensors.

#### objectmaybe_batch(IDictionary<object, object> tensors, IEnumerable<bool> keep_input, int batch_size, int num_threads, int capacity, bool enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, object shared_name, string name)

Conditionally creates batches of tensors based on keep_input. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

See docstring in batch for more details.
##### Parameters
IDictionary<object, object> tensors
The list or dictionary of tensors to enqueue.
IEnumerable<bool> keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
int batch_size
The new batch size pulled from the queue.
The number of threads enqueuing tensors. The batching will be nondeterministic if num_threads > 1.
int capacity
An integer. The maximum number of elements in the queue.
bool enqueue_many
Whether each tensor in tensors is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same types as tensors.

#### objectmaybe_batch(IEnumerable<object> tensors, IEnumerable<bool> keep_input, int batch_size, int num_threads, int capacity, bool enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, object shared_name, string name)

Conditionally creates batches of tensors based on keep_input. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

See docstring in batch for more details.
##### Parameters
IEnumerable<object> tensors
The list or dictionary of tensors to enqueue.
IEnumerable<bool> keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
int batch_size
The new batch size pulled from the queue.
The number of threads enqueuing tensors. The batching will be nondeterministic if num_threads > 1.
int capacity
An integer. The maximum number of elements in the queue.
bool enqueue_many
Whether each tensor in tensors is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same types as tensors.

#### objectmaybe_batch_dyn(object tensors, object keep_input, object batch_size, ImplicitContainer<T> num_threads, ImplicitContainer<T> capacity, ImplicitContainer<T> enqueue_many, object shapes, ImplicitContainer<T> dynamic_pad, ImplicitContainer<T> allow_smaller_final_batch, object shared_name, object name)

Conditionally creates batches of tensors based on keep_input. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

See docstring in batch for more details.
##### Parameters
object tensors
The list or dictionary of tensors to enqueue.
object keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
object batch_size
The new batch size pulled from the queue.
The number of threads enqueuing tensors. The batching will be nondeterministic if num_threads > 1.
ImplicitContainer<T> capacity
An integer. The maximum number of elements in the queue.
ImplicitContainer<T> enqueue_many
Whether each tensor in tensors is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
ImplicitContainer<T> allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional). If set, this queue will be shared under the given name across multiple sessions.
object name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same types as tensors.

#### objectmaybe_batch_join(IEnumerable<object> tensors_list, IGraphNodeBase keep_input, int batch_size, int capacity, bool enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, object shared_name, string name)

Runs a list of tensors to conditionally fill a queue to create batches. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).filter(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

See docstring in batch_join for more details.
##### Parameters
IEnumerable<object> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
IGraphNodeBase keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
int batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
bool enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list_list[i].
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectmaybe_batch_join(IEnumerable<object> tensors_list, IEnumerable<bool> keep_input, int batch_size, int capacity, bool enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, object shared_name, string name)

Runs a list of tensors to conditionally fill a queue to create batches. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).filter(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

See docstring in batch_join for more details.
##### Parameters
IEnumerable<object> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
IEnumerable<bool> keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
int batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
bool enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list_list[i].
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectmaybe_batch_join(IEnumerable<object> tensors_list, bool keep_input, int batch_size, int capacity, bool enqueue_many, object shapes, bool dynamic_pad, bool allow_smaller_final_batch, object shared_name, string name)

Runs a list of tensors to conditionally fill a queue to create batches. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).filter(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

See docstring in batch_join for more details.
##### Parameters
IEnumerable<object> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
bool keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
int batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
bool enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list_list[i].
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectmaybe_batch_join_dyn(object tensors_list, object keep_input, object batch_size, ImplicitContainer<T> capacity, ImplicitContainer<T> enqueue_many, object shapes, ImplicitContainer<T> dynamic_pad, ImplicitContainer<T> allow_smaller_final_batch, object shared_name, object name)

Runs a list of tensors to conditionally fill a queue to create batches. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).filter(...).batch(batch_size) (or padded_batch(...) if dynamic_pad=True).

See docstring in batch_join for more details.
##### Parameters
object tensors_list
A list of tuples or dictionaries of tensors to enqueue.
object keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
object batch_size
An integer. The new batch size pulled from the queue.
ImplicitContainer<T> capacity
An integer. The maximum number of elements in the queue.
ImplicitContainer<T> enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list_list[i].
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
ImplicitContainer<T> allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
object name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectmaybe_shuffle_batch(IEnumerable<object> tensors, int batch_size, int capacity, int min_after_dequeue, IEnumerable<bool> keep_input, int num_threads, Nullable<int> seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, object shared_name, string name)

Creates batches by randomly shuffling conditionally-enqueued tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).shuffle(min_after_dequeue).batch(batch_size).

See docstring in shuffle_batch for more details.
##### Parameters
IEnumerable<object> tensors
The list or dictionary of tensors to enqueue.
int batch_size
The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
IEnumerable<bool> keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
The number of threads enqueuing tensor_list.
Nullable<int> seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the types as tensors.

#### objectmaybe_shuffle_batch(IEnumerable<object> tensors, int batch_size, int capacity, int min_after_dequeue, IGraphNodeBase keep_input, int num_threads, Nullable<int> seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, object shared_name, string name)

Creates batches by randomly shuffling conditionally-enqueued tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).shuffle(min_after_dequeue).batch(batch_size).

See docstring in shuffle_batch for more details.
##### Parameters
IEnumerable<object> tensors
The list or dictionary of tensors to enqueue.
int batch_size
The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
IGraphNodeBase keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
The number of threads enqueuing tensor_list.
Nullable<int> seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the types as tensors.

#### objectmaybe_shuffle_batch(IDictionary<object, object> tensors, int batch_size, int capacity, int min_after_dequeue, bool keep_input, int num_threads, Nullable<int> seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, object shared_name, string name)

Creates batches by randomly shuffling conditionally-enqueued tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).shuffle(min_after_dequeue).batch(batch_size).

See docstring in shuffle_batch for more details.
##### Parameters
IDictionary<object, object> tensors
The list or dictionary of tensors to enqueue.
int batch_size
The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
bool keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
The number of threads enqueuing tensor_list.
Nullable<int> seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the types as tensors.

#### objectmaybe_shuffle_batch(IDictionary<object, object> tensors, int batch_size, int capacity, int min_after_dequeue, IEnumerable<bool> keep_input, int num_threads, Nullable<int> seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, object shared_name, string name)

Creates batches by randomly shuffling conditionally-enqueued tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).shuffle(min_after_dequeue).batch(batch_size).

See docstring in shuffle_batch for more details.
##### Parameters
IDictionary<object, object> tensors
The list or dictionary of tensors to enqueue.
int batch_size
The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
IEnumerable<bool> keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
The number of threads enqueuing tensor_list.
Nullable<int> seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the types as tensors.

#### objectmaybe_shuffle_batch(IDictionary<object, object> tensors, int batch_size, int capacity, int min_after_dequeue, IGraphNodeBase keep_input, int num_threads, Nullable<int> seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, object shared_name, string name)

Creates batches by randomly shuffling conditionally-enqueued tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).shuffle(min_after_dequeue).batch(batch_size).

See docstring in shuffle_batch for more details.
##### Parameters
IDictionary<object, object> tensors
The list or dictionary of tensors to enqueue.
int batch_size
The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
IGraphNodeBase keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
The number of threads enqueuing tensor_list.
Nullable<int> seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the types as tensors.

#### objectmaybe_shuffle_batch(IEnumerable<object> tensors, int batch_size, int capacity, int min_after_dequeue, bool keep_input, int num_threads, Nullable<int> seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, object shared_name, string name)

Creates batches by randomly shuffling conditionally-enqueued tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).shuffle(min_after_dequeue).batch(batch_size).

See docstring in shuffle_batch for more details.
##### Parameters
IEnumerable<object> tensors
The list or dictionary of tensors to enqueue.
int batch_size
The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
bool keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
The number of threads enqueuing tensor_list.
Nullable<int> seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the types as tensors.

#### objectmaybe_shuffle_batch_dyn(object tensors, object batch_size, object capacity, object min_after_dequeue, object keep_input, ImplicitContainer<T> num_threads, object seed, ImplicitContainer<T> enqueue_many, object shapes, ImplicitContainer<T> allow_smaller_final_batch, object shared_name, object name)

Creates batches by randomly shuffling conditionally-enqueued tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.filter(...).shuffle(min_after_dequeue).batch(batch_size).

See docstring in shuffle_batch for more details.
##### Parameters
object tensors
The list or dictionary of tensors to enqueue.
object batch_size
The new batch size pulled from the queue.
object capacity
An integer. The maximum number of elements in the queue.
object min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
object keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
The number of threads enqueuing tensor_list.
object seed
Seed for the random shuffling within the queue.
ImplicitContainer<T> enqueue_many
Whether each tensor in tensor_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list.
ImplicitContainer<T> allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
object name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the types as tensors.

#### objectmaybe_shuffle_batch_join(IEnumerable<object> tensors_list, int batch_size, int capacity, int min_after_dequeue, IGraphNodeBase keep_input, object seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, object shared_name, string name)

Create batches by randomly shuffling conditionally-enqueued tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).filter(...).shuffle(min_after_dequeue).batch(batch_size).

See docstring in shuffle_batch_join for more details.
##### Parameters
IEnumerable<object> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
int batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
IGraphNodeBase keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
object seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectmaybe_shuffle_batch_join(IEnumerable<object> tensors_list, int batch_size, int capacity, int min_after_dequeue, IEnumerable<bool> keep_input, object seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, object shared_name, string name)

Create batches by randomly shuffling conditionally-enqueued tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).filter(...).shuffle(min_after_dequeue).batch(batch_size).

See docstring in shuffle_batch_join for more details.
##### Parameters
IEnumerable<object> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
int batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
IEnumerable<bool> keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
object seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectmaybe_shuffle_batch_join(IEnumerable<object> tensors_list, int batch_size, int capacity, int min_after_dequeue, bool keep_input, object seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, object shared_name, string name)

Create batches by randomly shuffling conditionally-enqueued tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).filter(...).shuffle(min_after_dequeue).batch(batch_size).

See docstring in shuffle_batch_join for more details.
##### Parameters
IEnumerable<object> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
int batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
bool keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
object seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectmaybe_shuffle_batch_join_dyn(object tensors_list, object batch_size, object capacity, object min_after_dequeue, object keep_input, object seed, ImplicitContainer<T> enqueue_many, object shapes, ImplicitContainer<T> allow_smaller_final_batch, object shared_name, object name)

Create batches by randomly shuffling conditionally-enqueued tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).filter(...).shuffle(min_after_dequeue).batch(batch_size).

See docstring in shuffle_batch_join for more details.
##### Parameters
object tensors_list
A list of tuples or dictionaries of tensors to enqueue.
object batch_size
An integer. The new batch size pulled from the queue.
object capacity
An integer. The maximum number of elements in the queue.
object min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
object keep_input
A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
object seed
Seed for the random shuffling within the queue.
ImplicitContainer<T> enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
ImplicitContainer<T> allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
object name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, Byte[] checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, int save_checkpoint_secs, int save_summaries_steps, ImplicitContainer<T> save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
Byte[] checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
int save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
int save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
ImplicitContainer<T> save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, Byte[] checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, int save_checkpoint_secs, int save_summaries_steps, double save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
Byte[] checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
int save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
int save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
double save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, Byte[] checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, int save_checkpoint_secs, ImplicitContainer<T> save_summaries_steps, ImplicitContainer<T> save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
Byte[] checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
int save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
ImplicitContainer<T> save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
ImplicitContainer<T> save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, Byte[] checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, ImplicitContainer<T> save_checkpoint_secs, int save_summaries_steps, double save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
Byte[] checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
ImplicitContainer<T> save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
int save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
double save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, Byte[] checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, ImplicitContainer<T> save_checkpoint_secs, int save_summaries_steps, ImplicitContainer<T> save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
Byte[] checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
ImplicitContainer<T> save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
int save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
ImplicitContainer<T> save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, Byte[] checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, double save_checkpoint_secs, ImplicitContainer<T> save_summaries_steps, double save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
Byte[] checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
double save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
ImplicitContainer<T> save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
double save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, Byte[] checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, ImplicitContainer<T> save_checkpoint_secs, ImplicitContainer<T> save_summaries_steps, ImplicitContainer<T> save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
Byte[] checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
ImplicitContainer<T> save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
ImplicitContainer<T> save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
ImplicitContainer<T> save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, Byte[] checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, ImplicitContainer<T> save_checkpoint_secs, ImplicitContainer<T> save_summaries_steps, double save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
Byte[] checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
ImplicitContainer<T> save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
ImplicitContainer<T> save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
double save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, Byte[] checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, double save_checkpoint_secs, int save_summaries_steps, ImplicitContainer<T> save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
Byte[] checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
double save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
int save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
ImplicitContainer<T> save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, Byte[] checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, double save_checkpoint_secs, int save_summaries_steps, double save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
Byte[] checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
double save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
int save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
double save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, Byte[] checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, int save_checkpoint_secs, ImplicitContainer<T> save_summaries_steps, double save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
Byte[] checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
int save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
ImplicitContainer<T> save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
double save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, string checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, double save_checkpoint_secs, ImplicitContainer<T> save_summaries_steps, double save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
string checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
double save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
ImplicitContainer<T> save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
double save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, Byte[] checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, double save_checkpoint_secs, ImplicitContainer<T> save_summaries_steps, ImplicitContainer<T> save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
Byte[] checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
double save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
ImplicitContainer<T> save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
ImplicitContainer<T> save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, string checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, double save_checkpoint_secs, int save_summaries_steps, double save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
string checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
double save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
int save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
double save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, string checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, double save_checkpoint_secs, int save_summaries_steps, ImplicitContainer<T> save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
string checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
double save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
int save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
ImplicitContainer<T> save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, string checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, ImplicitContainer<T> save_checkpoint_secs, ImplicitContainer<T> save_summaries_steps, double save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
string checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
ImplicitContainer<T> save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
ImplicitContainer<T> save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
double save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, string checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, int save_checkpoint_secs, int save_summaries_steps, ImplicitContainer<T> save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
string checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
int save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
int save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
ImplicitContainer<T> save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, string checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, ImplicitContainer<T> save_checkpoint_secs, ImplicitContainer<T> save_summaries_steps, ImplicitContainer<T> save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
string checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
ImplicitContainer<T> save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
ImplicitContainer<T> save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
ImplicitContainer<T> save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, string checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, ImplicitContainer<T> save_checkpoint_secs, int save_summaries_steps, double save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
string checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
ImplicitContainer<T> save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
int save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
double save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, string checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, ImplicitContainer<T> save_checkpoint_secs, int save_summaries_steps, ImplicitContainer<T> save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
string checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
ImplicitContainer<T> save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
int save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
ImplicitContainer<T> save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, string checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, int save_checkpoint_secs, ImplicitContainer<T> save_summaries_steps, double save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
string checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
int save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
ImplicitContainer<T> save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
double save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, string checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, int save_checkpoint_secs, int save_summaries_steps, double save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
string checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
int save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
int save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
double save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, string checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, double save_checkpoint_secs, ImplicitContainer<T> save_summaries_steps, ImplicitContainer<T> save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
string checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
double save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
ImplicitContainer<T> save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
ImplicitContainer<T> save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### MonitoredSessionMonitoredTrainingSession(string master, Nullable<bool> is_chief, string checkpoint_dir, Scaffold scaffold, IEnumerable<object> hooks, IEnumerable<SessionRunHook> chief_only_hooks, int save_checkpoint_secs, ImplicitContainer<T> save_summaries_steps, ImplicitContainer<T> save_summaries_secs, object config, int stop_grace_period_secs, Nullable<int> log_step_count_steps, int max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
string master
String the TensorFlow master to use.
Nullable<bool> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
string checkpoint_dir
A string. Optional path to a directory where to restore variables.
Scaffold scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
IEnumerable<object> hooks
Optional list of SessionRunHook objects.
IEnumerable<SessionRunHook> chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
int save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
ImplicitContainer<T> save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
ImplicitContainer<T> save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
int stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
Nullable<int> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
int max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
MonitoredSession
A MonitoredSession object.

#### objectMonitoredTrainingSession_dyn(ImplicitContainer<T> master, ImplicitContainer<T> is_chief, object checkpoint_dir, object scaffold, object hooks, object chief_only_hooks, ImplicitContainer<T> save_checkpoint_secs, ImplicitContainer<T> save_summaries_steps, ImplicitContainer<T> save_summaries_secs, object config, ImplicitContainer<T> stop_grace_period_secs, ImplicitContainer<T> log_step_count_steps, ImplicitContainer<T> max_wait_secs, ImplicitContainer<T> save_checkpoint_steps, object summary_dir)

Creates a MonitoredSession for training.

For a chief, this utility sets proper session initializer/restorer. It also creates hooks related to checkpoint and summary saving. For workers, this utility sets proper session creator which waits for the chief to initialize/restore. Please check tf.compat.v1.train.MonitoredSession for more information.
##### Parameters
ImplicitContainer<T> master
String the TensorFlow master to use.
ImplicitContainer<T> is_chief
If True, it will take care of initialization and recovery the underlying TensorFlow session. If False, it will wait on a chief to initialize or recover the TensorFlow session.
object checkpoint_dir
A string. Optional path to a directory where to restore variables.
object scaffold
A Scaffold used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph.
object hooks
Optional list of SessionRunHook objects.
object chief_only_hooks
list of SessionRunHook objects. Activate these hooks if is_chief==True, ignore otherwise.
ImplicitContainer<T> save_checkpoint_secs
The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default 600.
ImplicitContainer<T> save_summaries_steps
The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default 100.
ImplicitContainer<T> save_summaries_secs
The frequency, in secs, that the summaries are written to disk using a default summary saver. If both save_summaries_steps and save_summaries_secs are set to None, then the default summary saver isn't used. Default not enabled.
object config
an instance of tf.compat.v1.ConfigProto proto used to configure the session. It's the config argument of constructor of tf.compat.v1.Session.
ImplicitContainer<T> stop_grace_period_secs
Number of seconds given to threads to stop after close() has been called.
ImplicitContainer<T> log_step_count_steps
The frequency, in number of global steps, that the global step/sec is logged.
ImplicitContainer<T> max_wait_secs
Maximum time workers should wait for the session to become available. This should be kept relatively short to help detect incorrect code, but sometimes may need to be increased if the chief takes a while to start up.
ImplicitContainer<T> save_checkpoint_steps
The frequency, in number of global steps, that a checkpoint is saved using a default checkpoint saver. If both save_checkpoint_steps and save_checkpoint_secs are set to None, then the default checkpoint saver isn't used. If both are provided, then only save_checkpoint_secs is used. Default not enabled.
object summary_dir
A string. Optional path to a directory where to save summaries. If None, checkpoint_dir is used instead.
##### Returns
object
A MonitoredSession object.

#### objectnatural_exp_decay(double learning_rate, ResourceVariable global_step, int decay_steps, double decay_rate, bool staircase, string name)

Applies natural exponential decay to the initial learning rate.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies an exponential decay function to a provided initial learning rate. It requires an global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: or, if staircase is True, as: Example: decay exponentially with a base of 0.96:
##### Parameters
double learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
ResourceVariable global_step
A Python number. Global step to use for the decay computation. Must not be negative.
int decay_steps
How often to apply decay.
double decay_rate
A Python number. The decay rate.
bool staircase
Whether to apply decay in a discrete staircase, as opposed to continuous, fashion.
string name
String. Optional name of the operation. Defaults to 'ExponentialTimeDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
decayed_learning_rate = learning_rate * exp(-decay_rate * global_step /
decay_step)

#### objectnatural_exp_decay_dyn(object learning_rate, object global_step, object decay_steps, object decay_rate, ImplicitContainer<T> staircase, object name)

Applies natural exponential decay to the initial learning rate.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies an exponential decay function to a provided initial learning rate. It requires an global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: or, if staircase is True, as: Example: decay exponentially with a base of 0.96:
##### Parameters
object learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
object global_step
A Python number. Global step to use for the decay computation. Must not be negative.
object decay_steps
How often to apply decay.
object decay_rate
A Python number. The decay rate.
ImplicitContainer<T> staircase
Whether to apply decay in a discrete staircase, as opposed to continuous, fashion.
object name
String. Optional name of the operation. Defaults to 'ExponentialTimeDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
decayed_learning_rate = learning_rate * exp(-decay_rate * global_step /
decay_step)

#### objectnoisy_linear_cosine_decay(double learning_rate, int global_step, int decay_steps, double initial_variance, double variance_decay, int num_periods, double alpha, double beta, string name)

Applies noisy linear cosine decay to the learning rate.

See [Bello et al., ICML2017] Neural Optimizer Search with RL. https://arxiv.org/abs/1709.07417

For the idea of warm starts here controlled by num_periods, see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

Note that linear cosine decay is more aggressive than cosine decay and larger initial learning rates can typically be used.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies a noisy linear cosine decay function to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: where eps_t is 0-centered gaussian noise with variance initial_variance / (1 + global_step) ** variance_decay

Example usage:
##### Parameters
double learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
int global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation.
int decay_steps
A scalar int32 or int64 Tensor or a Python number. Number of steps to decay over.
double initial_variance
initial variance for the noise. See computation above.
double variance_decay
decay for the noise's variance. See computation above.
int num_periods
Number of periods in the cosine part of the decay. See computation above.
double alpha
See computation above.
double beta
See computation above.
string name
String. Optional name of the operation. Defaults to 'NoisyLinearCosineDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
global_step = min(global_step, decay_steps)
linear_decay = (decay_steps - global_step) / decay_steps)
cosine_decay = 0.5 * (
1 + cos(pi * 2 * num_periods * global_step / decay_steps))
decayed = (alpha + linear_decay + eps_t) * cosine_decay + beta
decayed_learning_rate = learning_rate * decayed

#### objectnoisy_linear_cosine_decay(double learning_rate, int global_step, int decay_steps, double initial_variance, double variance_decay, double num_periods, double alpha, double beta, string name)

Applies noisy linear cosine decay to the learning rate.

See [Bello et al., ICML2017] Neural Optimizer Search with RL. https://arxiv.org/abs/1709.07417

For the idea of warm starts here controlled by num_periods, see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

Note that linear cosine decay is more aggressive than cosine decay and larger initial learning rates can typically be used.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies a noisy linear cosine decay function to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: where eps_t is 0-centered gaussian noise with variance initial_variance / (1 + global_step) ** variance_decay

Example usage:
##### Parameters
double learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
int global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation.
int decay_steps
A scalar int32 or int64 Tensor or a Python number. Number of steps to decay over.
double initial_variance
initial variance for the noise. See computation above.
double variance_decay
decay for the noise's variance. See computation above.
double num_periods
Number of periods in the cosine part of the decay. See computation above.
double alpha
See computation above.
double beta
See computation above.
string name
String. Optional name of the operation. Defaults to 'NoisyLinearCosineDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
global_step = min(global_step, decay_steps)
linear_decay = (decay_steps - global_step) / decay_steps)
cosine_decay = 0.5 * (
1 + cos(pi * 2 * num_periods * global_step / decay_steps))
decayed = (alpha + linear_decay + eps_t) * cosine_decay + beta
decayed_learning_rate = learning_rate * decayed

#### objectnoisy_linear_cosine_decay_dyn(object learning_rate, object global_step, object decay_steps, ImplicitContainer<T> initial_variance, ImplicitContainer<T> variance_decay, ImplicitContainer<T> num_periods, ImplicitContainer<T> alpha, ImplicitContainer<T> beta, object name)

Applies noisy linear cosine decay to the learning rate.

See [Bello et al., ICML2017] Neural Optimizer Search with RL. https://arxiv.org/abs/1709.07417

For the idea of warm starts here controlled by num_periods, see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

Note that linear cosine decay is more aggressive than cosine decay and larger initial learning rates can typically be used.

When training a model, it is often recommended to lower the learning rate as the training progresses. This function applies a noisy linear cosine decay function to a provided initial learning rate. It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: where eps_t is 0-centered gaussian noise with variance initial_variance / (1 + global_step) ** variance_decay

Example usage:
##### Parameters
object learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
object global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation.
object decay_steps
A scalar int32 or int64 Tensor or a Python number. Number of steps to decay over.
ImplicitContainer<T> initial_variance
initial variance for the noise. See computation above.
ImplicitContainer<T> variance_decay
decay for the noise's variance. See computation above.
ImplicitContainer<T> num_periods
Number of periods in the cosine part of the decay. See computation above.
ImplicitContainer<T> alpha
See computation above.
ImplicitContainer<T> beta
See computation above.
object name
String. Optional name of the operation. Defaults to 'NoisyLinearCosineDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
global_step = min(global_step, decay_steps)
linear_decay = (decay_steps - global_step) / decay_steps)
cosine_decay = 0.5 * (
1 + cos(pi * 2 * num_periods * global_step / decay_steps))
decayed = (alpha + linear_decay + eps_t) * cosine_decay + beta
decayed_learning_rate = learning_rate * decayed

#### objectpiecewise_constant(Variable x, IEnumerable<double> boundaries, IEnumerable<int> values, string name)

Piecewise constant from boundaries and interval values.

Example: use a learning rate that's 1.0 for the first 100001 steps, 0.5 for the next 10000 steps, and 0.1 for any additional steps.
##### Parameters
Variable x
A 0-D scalar Tensor. Must be one of the following types: float32, float64, uint8, int8, int16, int32, int64.
IEnumerable<double> boundaries
A list of Tensors or ints or floats with strictly increasing entries, and with all elements having the same type as x.
IEnumerable<int> values
A list of Tensors or floats or ints that specifies the values for the intervals defined by boundaries. It should have one more element than boundaries, and all elements should have the same type.
string name
A string. Optional name of the operation. Defaults to 'PiecewiseConstant'.
##### Returns
object
A 0-D Tensor. Its value is values[0] when x <= boundaries[0], values[1] when x > boundaries[0] and x <= boundaries[1],..., and values[-1] when x > boundaries[-1].
Show Example
global_step = tf.Variable(0, trainable=False)
boundaries = [100000, 110000]
values = [1.0, 0.5, 0.1]
learning_rate = tf.compat.v1.train.piecewise_constant(global_step, boundaries,
values)

# Later, whenever we perform an optimization step, we increment global_step.

#### objectpiecewise_constant_dyn(object x, object boundaries, object values, object name)

Piecewise constant from boundaries and interval values.

Example: use a learning rate that's 1.0 for the first 100001 steps, 0.5 for the next 10000 steps, and 0.1 for any additional steps.
##### Parameters
object x
A 0-D scalar Tensor. Must be one of the following types: float32, float64, uint8, int8, int16, int32, int64.
object boundaries
A list of Tensors or ints or floats with strictly increasing entries, and with all elements having the same type as x.
object values
A list of Tensors or floats or ints that specifies the values for the intervals defined by boundaries. It should have one more element than boundaries, and all elements should have the same type.
object name
A string. Optional name of the operation. Defaults to 'PiecewiseConstant'.
##### Returns
object
A 0-D Tensor. Its value is values[0] when x <= boundaries[0], values[1] when x > boundaries[0] and x <= boundaries[1],..., and values[-1] when x > boundaries[-1].
Show Example
global_step = tf.Variable(0, trainable=False)
boundaries = [100000, 110000]
values = [1.0, 0.5, 0.1]
learning_rate = tf.compat.v1.train.piecewise_constant(global_step, boundaries,
values)

# Later, whenever we perform an optimization step, we increment global_step.

#### objectpolynomial_decay(double learning_rate, int global_step, int decay_steps, double end_learning_rate, double power, bool cycle, string name)

Applies a polynomial decay to the learning rate.

It is commonly observed that a monotonically decreasing learning rate, whose degree of change is carefully chosen, results in a better performing model. This function applies a polynomial decay function to a provided initial learning_rate to reach an end_learning_rate in the given decay_steps.

It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: If cycle is True then a multiple of decay_steps is used, the first one that is bigger than global_steps. Example: decay from 0.1 to 0.01 in 10000 steps using sqrt (i.e. power=0.5):
##### Parameters
double learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
int global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation. Must not be negative.
int decay_steps
A scalar int32 or int64 Tensor or a Python number. Must be positive. See the decay computation above.
double end_learning_rate
A scalar float32 or float64 Tensor or a Python number. The minimal end learning rate.
double power
A scalar float32 or float64 Tensor or a Python number. The power of the polynomial. Defaults to linear, 1.0.
bool cycle
A boolean, whether or not it should cycle beyond decay_steps.
string name
String. Optional name of the operation. Defaults to 'PolynomialDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
global_step = min(global_step, decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
(1 - global_step / decay_steps) ^ (power) +
end_learning_rate

#### objectpolynomial_decay_dyn(object learning_rate, object global_step, object decay_steps, ImplicitContainer<T> end_learning_rate, ImplicitContainer<T> power, ImplicitContainer<T> cycle, object name)

Applies a polynomial decay to the learning rate.

It is commonly observed that a monotonically decreasing learning rate, whose degree of change is carefully chosen, results in a better performing model. This function applies a polynomial decay function to a provided initial learning_rate to reach an end_learning_rate in the given decay_steps.

It requires a global_step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The function returns the decayed learning rate. It is computed as: If cycle is True then a multiple of decay_steps is used, the first one that is bigger than global_steps. Example: decay from 0.1 to 0.01 in 10000 steps using sqrt (i.e. power=0.5):
##### Parameters
object learning_rate
A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
object global_step
A scalar int32 or int64 Tensor or a Python number. Global step to use for the decay computation. Must not be negative.
object decay_steps
A scalar int32 or int64 Tensor or a Python number. Must be positive. See the decay computation above.
ImplicitContainer<T> end_learning_rate
A scalar float32 or float64 Tensor or a Python number. The minimal end learning rate.
ImplicitContainer<T> power
A scalar float32 or float64 Tensor or a Python number. The power of the polynomial. Defaults to linear, 1.0.
ImplicitContainer<T> cycle
A boolean, whether or not it should cycle beyond decay_steps.
object name
String. Optional name of the operation. Defaults to 'PolynomialDecay'.
##### Returns
object
A scalar Tensor of the same type as learning_rate. The decayed learning rate.
Show Example
global_step = min(global_step, decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
(1 - global_step / decay_steps) ^ (power) +
end_learning_rate

#### objectrange_input_producer(int limit, Nullable<int> num_epochs, bool shuffle, Nullable<int> seed, int capacity, string shared_name, string name)

Produces the integers from 0 to limit-1 in a queue. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.range(limit).shuffle(limit).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Note: if num_epochs is not None, this function creates local counter epochs. Use local_variables_initializer() to initialize local variables.
##### Parameters
int limit
An int32 scalar tensor.
Nullable<int> num_epochs
An integer (optional). If specified, range_input_producer produces each integer num_epochs times before generating an OutOfRange error. If not specified, range_input_producer can cycle through the integers an unlimited number of times.
bool shuffle
Boolean. If true, the integers are randomly shuffled within each epoch.
Nullable<int> seed
An integer (optional). Seed used if shuffle == True.
int capacity
An integer. Sets the queue capacity.
string shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
string name
A name for the operations (optional).
##### Returns
object
A Queue with the output integers. A QueueRunner for the Queue is added to the current Graph's QUEUE_RUNNER collection.

#### objectrange_input_producer_dyn(object limit, object num_epochs, ImplicitContainer<T> shuffle, object seed, ImplicitContainer<T> capacity, object shared_name, object name)

Produces the integers from 0 to limit-1 in a queue. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.range(limit).shuffle(limit).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Note: if num_epochs is not None, this function creates local counter epochs. Use local_variables_initializer() to initialize local variables.
##### Parameters
object limit
An int32 scalar tensor.
object num_epochs
An integer (optional). If specified, range_input_producer produces each integer num_epochs times before generating an OutOfRange error. If not specified, range_input_producer can cycle through the integers an unlimited number of times.
ImplicitContainer<T> shuffle
Boolean. If true, the integers are randomly shuffled within each epoch.
object seed
An integer (optional). Seed used if shuffle == True.
ImplicitContainer<T> capacity
An integer. Sets the queue capacity.
object shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
object name
A name for the operations (optional).
##### Returns
object
A Queue with the output integers. A QueueRunner for the Queue is added to the current Graph's QUEUE_RUNNER collection.

#### voidremove_checkpoint(Byte[] checkpoint_prefix, ImplicitContainer<T> checkpoint_format_version, string meta_graph_suffix)

Removes a checkpoint given by checkpoint_prefix. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use standard file APIs to delete files with this prefix.
##### Parameters
Byte[] checkpoint_prefix
The prefix of a V1 or V2 checkpoint. Typically the result of Saver.save() or that of tf.train.latest_checkpoint(), regardless of sharded/non-sharded or V1/V2.
ImplicitContainer<T> checkpoint_format_version
SaverDef.CheckpointFormatVersion, defaults to SaverDef.V2.
string meta_graph_suffix
Suffix for MetaGraphDef file. Defaults to 'meta'.

#### voidremove_checkpoint(IEnumerable<object> checkpoint_prefix, ImplicitContainer<T> checkpoint_format_version, string meta_graph_suffix)

Removes a checkpoint given by checkpoint_prefix. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use standard file APIs to delete files with this prefix.
##### Parameters
IEnumerable<object> checkpoint_prefix
The prefix of a V1 or V2 checkpoint. Typically the result of Saver.save() or that of tf.train.latest_checkpoint(), regardless of sharded/non-sharded or V1/V2.
ImplicitContainer<T> checkpoint_format_version
SaverDef.CheckpointFormatVersion, defaults to SaverDef.V2.
string meta_graph_suffix
Suffix for MetaGraphDef file. Defaults to 'meta'.

#### voidremove_checkpoint(string checkpoint_prefix, ImplicitContainer<T> checkpoint_format_version, string meta_graph_suffix)

Removes a checkpoint given by checkpoint_prefix. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use standard file APIs to delete files with this prefix.
##### Parameters
string checkpoint_prefix
The prefix of a V1 or V2 checkpoint. Typically the result of Saver.save() or that of tf.train.latest_checkpoint(), regardless of sharded/non-sharded or V1/V2.
ImplicitContainer<T> checkpoint_format_version
SaverDef.CheckpointFormatVersion, defaults to SaverDef.V2.
string meta_graph_suffix
Suffix for MetaGraphDef file. Defaults to 'meta'.

#### objectremove_checkpoint_dyn(object checkpoint_prefix, ImplicitContainer<T> checkpoint_format_version, ImplicitContainer<T> meta_graph_suffix)

Removes a checkpoint given by checkpoint_prefix. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use standard file APIs to delete files with this prefix.
##### Parameters
object checkpoint_prefix
The prefix of a V1 or V2 checkpoint. Typically the result of Saver.save() or that of tf.train.latest_checkpoint(), regardless of sharded/non-sharded or V1/V2.
ImplicitContainer<T> checkpoint_format_version
SaverDef.CheckpointFormatVersion, defaults to SaverDef.V2.
ImplicitContainer<T> meta_graph_suffix
Suffix for MetaGraphDef file. Defaults to 'meta'.

#### PythonFunctionContainerreplica_device_setter(int ps_tasks, string ps_device, string worker_device, bool merge_devices, IDictionary<object, object> cluster, IEnumerable<string> ps_ops, GreedyLoadBalancingStrategy ps_strategy)

Return a device function to use when building a Graph for replicas.

Device Functions are used in with tf.device(device_function): statement to automatically assign devices to Operation objects as they are constructed, Device constraints are added from the inner-most context first, working outwards. The merging behavior adds constraints to fields that are yet unset by a more inner context. Currently the fields are (job, task, cpu/gpu).

If cluster is None, and ps_tasks is 0, the returned function is a no-op. Otherwise, the value of ps_tasks is derived from cluster.

By default, only Variable ops are placed on ps tasks, and the placement strategy is round-robin over all ps tasks. A custom ps_strategy may be used to do more intelligent placement, such as tf.contrib.training.GreedyLoadBalancingStrategy.

For example,
##### Parameters
Number of tasks in the ps job. Ignored if cluster is provided.
string ps_device
String. Device of the ps job. If empty no ps job is used. Defaults to ps.
string worker_device
String. Device of the worker job. If empty no worker job is used.
bool merge_devices
Boolean. If True, merges or only sets a device if the device constraint is completely unset. merges device specification rather than overriding them.
IDictionary<object, object> cluster
ClusterDef proto or ClusterSpec.
IEnumerable<string> ps_ops
List of strings representing Operation types that need to be placed on ps devices. If None, defaults to STANDARD_PS_OPS.
A callable invoked for every ps Operation (i.e. matched by ps_ops), that takes the Operation and returns the ps task index to use. If None, defaults to a round-robin strategy across all ps devices.
##### Returns
PythonFunctionContainer
A function to pass to tf.device().
Show Example
# To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker
# jobs on hosts worker0, worker1 and worker2.
cluster_spec = {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
with
tf.device(tf.compat.v1.train.replica_device_setter(cluster=cluster_spec)):
v1 = tf.Variable(...)  # assigned to /job:ps/task:0
v2 = tf.Variable(...)  # assigned to /job:ps/task:1
v3 = tf.Variable(...)  # assigned to /job:ps/task:0
# Run compute

#### PythonFunctionContainerreplica_device_setter(int ps_tasks, string ps_device, string worker_device, bool merge_devices, IDictionary<object, object> cluster, IEnumerable<string> ps_ops, _OpRoundRobinStrategy ps_strategy)

Return a device function to use when building a Graph for replicas.

Device Functions are used in with tf.device(device_function): statement to automatically assign devices to Operation objects as they are constructed, Device constraints are added from the inner-most context first, working outwards. The merging behavior adds constraints to fields that are yet unset by a more inner context. Currently the fields are (job, task, cpu/gpu).

If cluster is None, and ps_tasks is 0, the returned function is a no-op. Otherwise, the value of ps_tasks is derived from cluster.

By default, only Variable ops are placed on ps tasks, and the placement strategy is round-robin over all ps tasks. A custom ps_strategy may be used to do more intelligent placement, such as tf.contrib.training.GreedyLoadBalancingStrategy.

For example,
##### Parameters
Number of tasks in the ps job. Ignored if cluster is provided.
string ps_device
String. Device of the ps job. If empty no ps job is used. Defaults to ps.
string worker_device
String. Device of the worker job. If empty no worker job is used.
bool merge_devices
Boolean. If True, merges or only sets a device if the device constraint is completely unset. merges device specification rather than overriding them.
IDictionary<object, object> cluster
ClusterDef proto or ClusterSpec.
IEnumerable<string> ps_ops
List of strings representing Operation types that need to be placed on ps devices. If None, defaults to STANDARD_PS_OPS.
_OpRoundRobinStrategy ps_strategy
A callable invoked for every ps Operation (i.e. matched by ps_ops), that takes the Operation and returns the ps task index to use. If None, defaults to a round-robin strategy across all ps devices.
##### Returns
PythonFunctionContainer
A function to pass to tf.device().
Show Example
# To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker
# jobs on hosts worker0, worker1 and worker2.
cluster_spec = {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
with
tf.device(tf.compat.v1.train.replica_device_setter(cluster=cluster_spec)):
v1 = tf.Variable(...)  # assigned to /job:ps/task:0
v2 = tf.Variable(...)  # assigned to /job:ps/task:1
v3 = tf.Variable(...)  # assigned to /job:ps/task:0
# Run compute

#### PythonFunctionContainerreplica_device_setter(int ps_tasks, string ps_device, string worker_device, bool merge_devices, IDictionary<object, object> cluster, IEnumerable<string> ps_ops, RandomStrategy ps_strategy)

Return a device function to use when building a Graph for replicas.

Device Functions are used in with tf.device(device_function): statement to automatically assign devices to Operation objects as they are constructed, Device constraints are added from the inner-most context first, working outwards. The merging behavior adds constraints to fields that are yet unset by a more inner context. Currently the fields are (job, task, cpu/gpu).

If cluster is None, and ps_tasks is 0, the returned function is a no-op. Otherwise, the value of ps_tasks is derived from cluster.

By default, only Variable ops are placed on ps tasks, and the placement strategy is round-robin over all ps tasks. A custom ps_strategy may be used to do more intelligent placement, such as tf.contrib.training.GreedyLoadBalancingStrategy.

For example,
##### Parameters
Number of tasks in the ps job. Ignored if cluster is provided.
string ps_device
String. Device of the ps job. If empty no ps job is used. Defaults to ps.
string worker_device
String. Device of the worker job. If empty no worker job is used.
bool merge_devices
Boolean. If True, merges or only sets a device if the device constraint is completely unset. merges device specification rather than overriding them.
IDictionary<object, object> cluster
ClusterDef proto or ClusterSpec.
IEnumerable<string> ps_ops
List of strings representing Operation types that need to be placed on ps devices. If None, defaults to STANDARD_PS_OPS.
RandomStrategy ps_strategy
A callable invoked for every ps Operation (i.e. matched by ps_ops), that takes the Operation and returns the ps task index to use. If None, defaults to a round-robin strategy across all ps devices.
##### Returns
PythonFunctionContainer
A function to pass to tf.device().
Show Example
# To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker
# jobs on hosts worker0, worker1 and worker2.
cluster_spec = {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
with
tf.device(tf.compat.v1.train.replica_device_setter(cluster=cluster_spec)):
v1 = tf.Variable(...)  # assigned to /job:ps/task:0
v2 = tf.Variable(...)  # assigned to /job:ps/task:1
v3 = tf.Variable(...)  # assigned to /job:ps/task:0
# Run compute

#### PythonFunctionContainerreplica_device_setter(int ps_tasks, string ps_device, string worker_device, bool merge_devices, ValueTuple<IDictionary<object, object>, PythonClassContainer> cluster, IEnumerable<string> ps_ops, GreedyLoadBalancingStrategy ps_strategy)

Return a device function to use when building a Graph for replicas.

Device Functions are used in with tf.device(device_function): statement to automatically assign devices to Operation objects as they are constructed, Device constraints are added from the inner-most context first, working outwards. The merging behavior adds constraints to fields that are yet unset by a more inner context. Currently the fields are (job, task, cpu/gpu).

If cluster is None, and ps_tasks is 0, the returned function is a no-op. Otherwise, the value of ps_tasks is derived from cluster.

By default, only Variable ops are placed on ps tasks, and the placement strategy is round-robin over all ps tasks. A custom ps_strategy may be used to do more intelligent placement, such as tf.contrib.training.GreedyLoadBalancingStrategy.

For example,
##### Parameters
Number of tasks in the ps job. Ignored if cluster is provided.
string ps_device
String. Device of the ps job. If empty no ps job is used. Defaults to ps.
string worker_device
String. Device of the worker job. If empty no worker job is used.
bool merge_devices
Boolean. If True, merges or only sets a device if the device constraint is completely unset. merges device specification rather than overriding them.
ValueTuple<IDictionary<object, object>, PythonClassContainer> cluster
ClusterDef proto or ClusterSpec.
IEnumerable<string> ps_ops
List of strings representing Operation types that need to be placed on ps devices. If None, defaults to STANDARD_PS_OPS.
A callable invoked for every ps Operation (i.e. matched by ps_ops), that takes the Operation and returns the ps task index to use. If None, defaults to a round-robin strategy across all ps devices.
##### Returns
PythonFunctionContainer
A function to pass to tf.device().
Show Example
# To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker
# jobs on hosts worker0, worker1 and worker2.
cluster_spec = {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
with
tf.device(tf.compat.v1.train.replica_device_setter(cluster=cluster_spec)):
v1 = tf.Variable(...)  # assigned to /job:ps/task:0
v2 = tf.Variable(...)  # assigned to /job:ps/task:1
v3 = tf.Variable(...)  # assigned to /job:ps/task:0
# Run compute

#### PythonFunctionContainerreplica_device_setter(int ps_tasks, string ps_device, string worker_device, bool merge_devices, ValueTuple<IDictionary<object, object>, PythonClassContainer> cluster, IEnumerable<string> ps_ops, RandomStrategy ps_strategy)

Return a device function to use when building a Graph for replicas.

Device Functions are used in with tf.device(device_function): statement to automatically assign devices to Operation objects as they are constructed, Device constraints are added from the inner-most context first, working outwards. The merging behavior adds constraints to fields that are yet unset by a more inner context. Currently the fields are (job, task, cpu/gpu).

If cluster is None, and ps_tasks is 0, the returned function is a no-op. Otherwise, the value of ps_tasks is derived from cluster.

By default, only Variable ops are placed on ps tasks, and the placement strategy is round-robin over all ps tasks. A custom ps_strategy may be used to do more intelligent placement, such as tf.contrib.training.GreedyLoadBalancingStrategy.

For example,
##### Parameters
Number of tasks in the ps job. Ignored if cluster is provided.
string ps_device
String. Device of the ps job. If empty no ps job is used. Defaults to ps.
string worker_device
String. Device of the worker job. If empty no worker job is used.
bool merge_devices
Boolean. If True, merges or only sets a device if the device constraint is completely unset. merges device specification rather than overriding them.
ValueTuple<IDictionary<object, object>, PythonClassContainer> cluster
ClusterDef proto or ClusterSpec.
IEnumerable<string> ps_ops
List of strings representing Operation types that need to be placed on ps devices. If None, defaults to STANDARD_PS_OPS.
RandomStrategy ps_strategy
A callable invoked for every ps Operation (i.e. matched by ps_ops), that takes the Operation and returns the ps task index to use. If None, defaults to a round-robin strategy across all ps devices.
##### Returns
PythonFunctionContainer
A function to pass to tf.device().
Show Example
# To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker
# jobs on hosts worker0, worker1 and worker2.
cluster_spec = {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
with
tf.device(tf.compat.v1.train.replica_device_setter(cluster=cluster_spec)):
v1 = tf.Variable(...)  # assigned to /job:ps/task:0
v2 = tf.Variable(...)  # assigned to /job:ps/task:1
v3 = tf.Variable(...)  # assigned to /job:ps/task:0
# Run compute

#### PythonFunctionContainerreplica_device_setter(int ps_tasks, string ps_device, string worker_device, bool merge_devices, ClusterSpec cluster, IEnumerable<string> ps_ops, _OpRoundRobinStrategy ps_strategy)

Return a device function to use when building a Graph for replicas.

Device Functions are used in with tf.device(device_function): statement to automatically assign devices to Operation objects as they are constructed, Device constraints are added from the inner-most context first, working outwards. The merging behavior adds constraints to fields that are yet unset by a more inner context. Currently the fields are (job, task, cpu/gpu).

If cluster is None, and ps_tasks is 0, the returned function is a no-op. Otherwise, the value of ps_tasks is derived from cluster.

By default, only Variable ops are placed on ps tasks, and the placement strategy is round-robin over all ps tasks. A custom ps_strategy may be used to do more intelligent placement, such as tf.contrib.training.GreedyLoadBalancingStrategy.

For example,
##### Parameters
Number of tasks in the ps job. Ignored if cluster is provided.
string ps_device
String. Device of the ps job. If empty no ps job is used. Defaults to ps.
string worker_device
String. Device of the worker job. If empty no worker job is used.
bool merge_devices
Boolean. If True, merges or only sets a device if the device constraint is completely unset. merges device specification rather than overriding them.
ClusterSpec cluster
ClusterDef proto or ClusterSpec.
IEnumerable<string> ps_ops
List of strings representing Operation types that need to be placed on ps devices. If None, defaults to STANDARD_PS_OPS.
_OpRoundRobinStrategy ps_strategy
A callable invoked for every ps Operation (i.e. matched by ps_ops), that takes the Operation and returns the ps task index to use. If None, defaults to a round-robin strategy across all ps devices.
##### Returns
PythonFunctionContainer
A function to pass to tf.device().
Show Example
# To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker
# jobs on hosts worker0, worker1 and worker2.
cluster_spec = {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
with
tf.device(tf.compat.v1.train.replica_device_setter(cluster=cluster_spec)):
v1 = tf.Variable(...)  # assigned to /job:ps/task:0
v2 = tf.Variable(...)  # assigned to /job:ps/task:1
v3 = tf.Variable(...)  # assigned to /job:ps/task:0
# Run compute

#### PythonFunctionContainerreplica_device_setter(int ps_tasks, string ps_device, string worker_device, bool merge_devices, ValueTuple<IDictionary<object, object>, PythonClassContainer> cluster, IEnumerable<string> ps_ops, _OpRoundRobinStrategy ps_strategy)

Return a device function to use when building a Graph for replicas.

Device Functions are used in with tf.device(device_function): statement to automatically assign devices to Operation objects as they are constructed, Device constraints are added from the inner-most context first, working outwards. The merging behavior adds constraints to fields that are yet unset by a more inner context. Currently the fields are (job, task, cpu/gpu).

If cluster is None, and ps_tasks is 0, the returned function is a no-op. Otherwise, the value of ps_tasks is derived from cluster.

By default, only Variable ops are placed on ps tasks, and the placement strategy is round-robin over all ps tasks. A custom ps_strategy may be used to do more intelligent placement, such as tf.contrib.training.GreedyLoadBalancingStrategy.

For example,
##### Parameters
Number of tasks in the ps job. Ignored if cluster is provided.
string ps_device
String. Device of the ps job. If empty no ps job is used. Defaults to ps.
string worker_device
String. Device of the worker job. If empty no worker job is used.
bool merge_devices
Boolean. If True, merges or only sets a device if the device constraint is completely unset. merges device specification rather than overriding them.
ValueTuple<IDictionary<object, object>, PythonClassContainer> cluster
ClusterDef proto or ClusterSpec.
IEnumerable<string> ps_ops
List of strings representing Operation types that need to be placed on ps devices. If None, defaults to STANDARD_PS_OPS.
_OpRoundRobinStrategy ps_strategy
A callable invoked for every ps Operation (i.e. matched by ps_ops), that takes the Operation and returns the ps task index to use. If None, defaults to a round-robin strategy across all ps devices.
##### Returns
PythonFunctionContainer
A function to pass to tf.device().
Show Example
# To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker
# jobs on hosts worker0, worker1 and worker2.
cluster_spec = {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
with
tf.device(tf.compat.v1.train.replica_device_setter(cluster=cluster_spec)):
v1 = tf.Variable(...)  # assigned to /job:ps/task:0
v2 = tf.Variable(...)  # assigned to /job:ps/task:1
v3 = tf.Variable(...)  # assigned to /job:ps/task:0
# Run compute

#### PythonFunctionContainerreplica_device_setter(int ps_tasks, string ps_device, string worker_device, bool merge_devices, ClusterSpec cluster, IEnumerable<string> ps_ops, GreedyLoadBalancingStrategy ps_strategy)

Return a device function to use when building a Graph for replicas.

Device Functions are used in with tf.device(device_function): statement to automatically assign devices to Operation objects as they are constructed, Device constraints are added from the inner-most context first, working outwards. The merging behavior adds constraints to fields that are yet unset by a more inner context. Currently the fields are (job, task, cpu/gpu).

If cluster is None, and ps_tasks is 0, the returned function is a no-op. Otherwise, the value of ps_tasks is derived from cluster.

By default, only Variable ops are placed on ps tasks, and the placement strategy is round-robin over all ps tasks. A custom ps_strategy may be used to do more intelligent placement, such as tf.contrib.training.GreedyLoadBalancingStrategy.

For example,
##### Parameters
Number of tasks in the ps job. Ignored if cluster is provided.
string ps_device
String. Device of the ps job. If empty no ps job is used. Defaults to ps.
string worker_device
String. Device of the worker job. If empty no worker job is used.
bool merge_devices
Boolean. If True, merges or only sets a device if the device constraint is completely unset. merges device specification rather than overriding them.
ClusterSpec cluster
ClusterDef proto or ClusterSpec.
IEnumerable<string> ps_ops
List of strings representing Operation types that need to be placed on ps devices. If None, defaults to STANDARD_PS_OPS.
A callable invoked for every ps Operation (i.e. matched by ps_ops), that takes the Operation and returns the ps task index to use. If None, defaults to a round-robin strategy across all ps devices.
##### Returns
PythonFunctionContainer
A function to pass to tf.device().
Show Example
# To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker
# jobs on hosts worker0, worker1 and worker2.
cluster_spec = {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
with
tf.device(tf.compat.v1.train.replica_device_setter(cluster=cluster_spec)):
v1 = tf.Variable(...)  # assigned to /job:ps/task:0
v2 = tf.Variable(...)  # assigned to /job:ps/task:1
v3 = tf.Variable(...)  # assigned to /job:ps/task:0
# Run compute

#### PythonFunctionContainerreplica_device_setter(int ps_tasks, string ps_device, string worker_device, bool merge_devices, ClusterSpec cluster, IEnumerable<string> ps_ops, RandomStrategy ps_strategy)

Return a device function to use when building a Graph for replicas.

Device Functions are used in with tf.device(device_function): statement to automatically assign devices to Operation objects as they are constructed, Device constraints are added from the inner-most context first, working outwards. The merging behavior adds constraints to fields that are yet unset by a more inner context. Currently the fields are (job, task, cpu/gpu).

If cluster is None, and ps_tasks is 0, the returned function is a no-op. Otherwise, the value of ps_tasks is derived from cluster.

By default, only Variable ops are placed on ps tasks, and the placement strategy is round-robin over all ps tasks. A custom ps_strategy may be used to do more intelligent placement, such as tf.contrib.training.GreedyLoadBalancingStrategy.

For example,
##### Parameters
Number of tasks in the ps job. Ignored if cluster is provided.
string ps_device
String. Device of the ps job. If empty no ps job is used. Defaults to ps.
string worker_device
String. Device of the worker job. If empty no worker job is used.
bool merge_devices
Boolean. If True, merges or only sets a device if the device constraint is completely unset. merges device specification rather than overriding them.
ClusterSpec cluster
ClusterDef proto or ClusterSpec.
IEnumerable<string> ps_ops
List of strings representing Operation types that need to be placed on ps devices. If None, defaults to STANDARD_PS_OPS.
RandomStrategy ps_strategy
A callable invoked for every ps Operation (i.e. matched by ps_ops), that takes the Operation and returns the ps task index to use. If None, defaults to a round-robin strategy across all ps devices.
##### Returns
PythonFunctionContainer
A function to pass to tf.device().
Show Example
# To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker
# jobs on hosts worker0, worker1 and worker2.
cluster_spec = {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
with
tf.device(tf.compat.v1.train.replica_device_setter(cluster=cluster_spec)):
v1 = tf.Variable(...)  # assigned to /job:ps/task:0
v2 = tf.Variable(...)  # assigned to /job:ps/task:1
v3 = tf.Variable(...)  # assigned to /job:ps/task:0
# Run compute

#### objectreplica_device_setter_dyn(ImplicitContainer<T> ps_tasks, ImplicitContainer<T> ps_device, ImplicitContainer<T> worker_device, ImplicitContainer<T> merge_devices, object cluster, object ps_ops, object ps_strategy)

Return a device function to use when building a Graph for replicas.

Device Functions are used in with tf.device(device_function): statement to automatically assign devices to Operation objects as they are constructed, Device constraints are added from the inner-most context first, working outwards. The merging behavior adds constraints to fields that are yet unset by a more inner context. Currently the fields are (job, task, cpu/gpu).

If cluster is None, and ps_tasks is 0, the returned function is a no-op. Otherwise, the value of ps_tasks is derived from cluster.

By default, only Variable ops are placed on ps tasks, and the placement strategy is round-robin over all ps tasks. A custom ps_strategy may be used to do more intelligent placement, such as tf.contrib.training.GreedyLoadBalancingStrategy.

For example,
##### Parameters
Number of tasks in the ps job. Ignored if cluster is provided.
ImplicitContainer<T> ps_device
String. Device of the ps job. If empty no ps job is used. Defaults to ps.
ImplicitContainer<T> worker_device
String. Device of the worker job. If empty no worker job is used.
ImplicitContainer<T> merge_devices
Boolean. If True, merges or only sets a device if the device constraint is completely unset. merges device specification rather than overriding them.
object cluster
ClusterDef proto or ClusterSpec.
object ps_ops
List of strings representing Operation types that need to be placed on ps devices. If None, defaults to STANDARD_PS_OPS.
object ps_strategy
A callable invoked for every ps Operation (i.e. matched by ps_ops), that takes the Operation and returns the ps task index to use. If None, defaults to a round-robin strategy across all ps devices.
##### Returns
object
A function to pass to tf.device().
Show Example
# To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker
# jobs on hosts worker0, worker1 and worker2.
cluster_spec = {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
with
tf.device(tf.compat.v1.train.replica_device_setter(cluster=cluster_spec)):
v1 = tf.Variable(...)  # assigned to /job:ps/task:0
v2 = tf.Variable(...)  # assigned to /job:ps/task:1
v3 = tf.Variable(...)  # assigned to /job:ps/task:0
# Run compute

#### Tensorsdca_fprint(IGraphNodeBase input, string name)

Computes fingerprints of the input strings.
##### Parameters
IGraphNodeBase input
A Tensor of type string. vector of strings to compute fingerprints on.
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor of type int64.

#### objectsdca_fprint_dyn(object input, object name)

Computes fingerprints of the input strings.
##### Parameters
object input
A Tensor of type string. vector of strings to compute fingerprints on.
object name
A name for the operation (optional).
##### Returns
object
A Tensor of type int64.

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, int loss_type, double l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
int loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
double l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, int loss_type, int l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
int loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
int l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, bool loss_type, string l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
bool loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
string l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, bool loss_type, double l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
bool loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
double l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, bool loss_type, bool l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
bool loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
bool l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, double loss_type, int l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
double loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
int l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, int loss_type, bool l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
int loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
bool l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, double loss_type, string l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
double loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
string l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, int loss_type, string l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
int loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
string l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, string loss_type, bool l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
string loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
bool l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, bool loss_type, int l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
bool loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
int l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, string loss_type, double l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
string loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
double l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, double loss_type, bool l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
double loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
bool l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, string loss_type, string l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
string loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
string l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, double loss_type, double l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
double loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
double l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer(IEnumerable<object> sparse_example_indices, IEnumerable<object> sparse_feature_indices, IEnumerable<object> sparse_feature_values, IEnumerable<IGraphNodeBase> dense_features, IGraphNodeBase example_weights, IGraphNodeBase example_labels, IEnumerable<object> sparse_indices, IEnumerable<IGraphNodeBase> sparse_weights, IEnumerable<IGraphNodeBase> dense_weights, IGraphNodeBase example_state_data, string loss_type, int l1, object l2, object num_loss_partitions, int num_inner_iterations, ImplicitContainer<T> adaptative, string name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
IEnumerable<object> sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
IEnumerable<object> sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
IEnumerable<object> sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
IEnumerable<IGraphNodeBase> dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
IGraphNodeBase example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
IGraphNodeBase example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
IEnumerable<object> sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
IEnumerable<IGraphNodeBase> sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
IEnumerable<IGraphNodeBase> dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
IGraphNodeBase example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
string loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
int l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
int num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
string name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_optimizer_dyn(object sparse_example_indices, object sparse_feature_indices, object sparse_feature_values, object dense_features, object example_weights, object example_labels, object sparse_indices, object sparse_weights, object dense_weights, object example_state_data, object loss_type, object l1, object l2, object num_loss_partitions, object num_inner_iterations, ImplicitContainer<T> adaptative, object name)

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
##### Parameters
object sparse_example_indices
A list of Tensor objects with type int64. a list of vectors which contain example indices.
object sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
object sparse_feature_values
A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
object dense_features
A list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
object example_weights
A Tensor of type float32. a vector which contains the weight associated with each example.
object example_labels
A Tensor of type float32. a vector which contains the label/target associated with each example.
object sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
object sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
object dense_weights
A list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
object example_state_data
A Tensor of type float32. a list of vectors containing the example state data.
object loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
object l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength.
object num_loss_partitions
An int that is >= 1. Number of partitions of the global loss function.
object num_inner_iterations
An int that is >= 1. Number of iterations per mini-batch.
An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
object name
A name for the operation (optional).
##### Returns
object
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).

#### objectsdca_shrink_l1(IEnumerable<IGraphNodeBase> weights, int l1, double l2, string name)

Applies L1 regularization shrink step on the parameters.
##### Parameters
IEnumerable<IGraphNodeBase> weights
A list of Tensor objects with type mutable float32. a list of vectors where each value is the weight associated with a feature group.
int l1
A float. Symmetric l1 regularization strength.
double l2
A float. Symmetric l2 regularization strength. Should be a positive float.
string name
A name for the operation (optional).
##### Returns
object
The created Operation.

#### objectsdca_shrink_l1(IEnumerable<IGraphNodeBase> weights, int l1, int l2, string name)

Applies L1 regularization shrink step on the parameters.
##### Parameters
IEnumerable<IGraphNodeBase> weights
A list of Tensor objects with type mutable float32. a list of vectors where each value is the weight associated with a feature group.
int l1
A float. Symmetric l1 regularization strength.
int l2
A float. Symmetric l2 regularization strength. Should be a positive float.
string name
A name for the operation (optional).
##### Returns
object
The created Operation.

#### objectsdca_shrink_l1_dyn(object weights, object l1, object l2, object name)

Applies L1 regularization shrink step on the parameters.
##### Parameters
object weights
A list of Tensor objects with type mutable float32. a list of vectors where each value is the weight associated with a feature group.
object l1
A float. Symmetric l1 regularization strength.
object l2
A float. Symmetric l2 regularization strength. Should be a positive float.
object name
A name for the operation (optional).
##### Returns
object
The created Operation.

#### objectshuffle_batch(IEnumerable<object> tensors, int batch_size, int capacity, int min_after_dequeue, int num_threads, Nullable<int> seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, string shared_name, string name)

Creates batches by randomly shuffling tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.shuffle(min_after_dequeue).batch(batch_size).

This function adds the following to the current Graph:

* A shuffling queue into which tensors from tensors are enqueued. * A dequeue_many operation to create batches from the queue. * A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors.

If enqueue_many is False, tensors is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself. *N.B.:* You must ensure that either (i) the shapes argument is passed, or (ii) all of the tensors in tensors must have fully-defined shapes. ValueError will be raised if neither of these conditions holds.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<object> tensors
The list or dictionary of tensors to enqueue.
int batch_size
The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
The number of threads enqueuing tensor_list.
Nullable<int> seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the types as tensors.
Show Example
# Creates batches of 32 images and 32 labels.
image_batch, label_batch = tf.compat.v1.train.shuffle_batch(
[single_image, single_label],
batch_size=32,
capacity=50000,
min_after_dequeue=10000)

#### objectshuffle_batch(IDictionary<string, string> tensors, int batch_size, int capacity, int min_after_dequeue, int num_threads, Nullable<int> seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, string shared_name, string name)

Creates batches by randomly shuffling tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.shuffle(min_after_dequeue).batch(batch_size).

This function adds the following to the current Graph:

* A shuffling queue into which tensors from tensors are enqueued. * A dequeue_many operation to create batches from the queue. * A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors.

If enqueue_many is False, tensors is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself. *N.B.:* You must ensure that either (i) the shapes argument is passed, or (ii) all of the tensors in tensors must have fully-defined shapes. ValueError will be raised if neither of these conditions holds.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IDictionary<string, string> tensors
The list or dictionary of tensors to enqueue.
int batch_size
The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
The number of threads enqueuing tensor_list.
Nullable<int> seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list.
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the types as tensors.
Show Example
# Creates batches of 32 images and 32 labels.
image_batch, label_batch = tf.compat.v1.train.shuffle_batch(
[single_image, single_label],
batch_size=32,
capacity=50000,
min_after_dequeue=10000)

#### objectshuffle_batch_dyn(object tensors, object batch_size, object capacity, object min_after_dequeue, ImplicitContainer<T> num_threads, object seed, ImplicitContainer<T> enqueue_many, object shapes, ImplicitContainer<T> allow_smaller_final_batch, object shared_name, object name)

Creates batches by randomly shuffling tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.shuffle(min_after_dequeue).batch(batch_size).

This function adds the following to the current Graph:

* A shuffling queue into which tensors from tensors are enqueued. * A dequeue_many operation to create batches from the queue. * A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors.

If enqueue_many is False, tensors is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself. *N.B.:* You must ensure that either (i) the shapes argument is passed, or (ii) all of the tensors in tensors must have fully-defined shapes. ValueError will be raised if neither of these conditions holds.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
object tensors
The list or dictionary of tensors to enqueue.
object batch_size
The new batch size pulled from the queue.
object capacity
An integer. The maximum number of elements in the queue.
object min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
The number of threads enqueuing tensor_list.
object seed
Seed for the random shuffling within the queue.
ImplicitContainer<T> enqueue_many
Whether each tensor in tensor_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list.
ImplicitContainer<T> allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(Optional) If set, this queue will be shared under the given name across multiple sessions.
object name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the types as tensors.
Show Example
# Creates batches of 32 images and 32 labels.
image_batch, label_batch = tf.compat.v1.train.shuffle_batch(
[single_image, single_label],
batch_size=32,
capacity=50000,
min_after_dequeue=10000)

#### objectshuffle_batch_join(IEnumerable<IDictionary<string, string>> tensors_list, int batch_size, int capacity, int min_after_dequeue, Nullable<int> seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, string shared_name, string name)

Create batches by randomly shuffling tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).shuffle(min_after_dequeue).batch(batch_size).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.shuffle_batch().

This version enqueues a different list of tensors in different threads. It adds the following to the current Graph:

* A shuffling queue into which tensors from tensors_list are enqueued. * A dequeue_many operation to create batches from the queue. * A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors_list.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<IDictionary<string, string>> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
int batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
Nullable<int> seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectshuffle_batch_join(IEnumerable<IDictionary<string, string>> tensors_list, int batch_size, int capacity, int min_after_dequeue, Nullable<int> seed, Nullable<int> enqueue_many, object shapes, bool allow_smaller_final_batch, string shared_name, string name)

Create batches by randomly shuffling tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).shuffle(min_after_dequeue).batch(batch_size).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.shuffle_batch().

This version enqueues a different list of tensors in different threads. It adds the following to the current Graph:

* A shuffling queue into which tensors from tensors_list are enqueued. * A dequeue_many operation to create batches from the queue. * A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors_list.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<IDictionary<string, string>> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
int batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
Nullable<int> seed
Seed for the random shuffling within the queue.
Nullable<int> enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectshuffle_batch_join(IEnumerable<IDictionary<string, string>> tensors_list, IGraphNodeBase batch_size, int capacity, int min_after_dequeue, Nullable<int> seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, string shared_name, string name)

Create batches by randomly shuffling tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).shuffle(min_after_dequeue).batch(batch_size).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.shuffle_batch().

This version enqueues a different list of tensors in different threads. It adds the following to the current Graph:

* A shuffling queue into which tensors from tensors_list are enqueued. * A dequeue_many operation to create batches from the queue. * A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors_list.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<IDictionary<string, string>> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
IGraphNodeBase batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
Nullable<int> seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectshuffle_batch_join(IEnumerable<IDictionary<string, string>> tensors_list, int batch_size, int capacity, IGraphNodeBase min_after_dequeue, Nullable<int> seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, string shared_name, string name)

Create batches by randomly shuffling tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).shuffle(min_after_dequeue).batch(batch_size).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.shuffle_batch().

This version enqueues a different list of tensors in different threads. It adds the following to the current Graph:

* A shuffling queue into which tensors from tensors_list are enqueued. * A dequeue_many operation to create batches from the queue. * A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors_list.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<IDictionary<string, string>> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
int batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
IGraphNodeBase min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
Nullable<int> seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectshuffle_batch_join(IEnumerable<IDictionary<string, string>> tensors_list, IGraphNodeBase batch_size, int capacity, int min_after_dequeue, Nullable<int> seed, Nullable<int> enqueue_many, object shapes, bool allow_smaller_final_batch, string shared_name, string name)

Create batches by randomly shuffling tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).shuffle(min_after_dequeue).batch(batch_size).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.shuffle_batch().

This version enqueues a different list of tensors in different threads. It adds the following to the current Graph:

* A shuffling queue into which tensors from tensors_list are enqueued. * A dequeue_many operation to create batches from the queue. * A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors_list.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<IDictionary<string, string>> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
IGraphNodeBase batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
int min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
Nullable<int> seed
Seed for the random shuffling within the queue.
Nullable<int> enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectshuffle_batch_join(IEnumerable<IDictionary<string, string>> tensors_list, IGraphNodeBase batch_size, int capacity, IGraphNodeBase min_after_dequeue, Nullable<int> seed, bool enqueue_many, object shapes, bool allow_smaller_final_batch, string shared_name, string name)

Create batches by randomly shuffling tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).shuffle(min_after_dequeue).batch(batch_size).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.shuffle_batch().

This version enqueues a different list of tensors in different threads. It adds the following to the current Graph:

* A shuffling queue into which tensors from tensors_list are enqueued. * A dequeue_many operation to create batches from the queue. * A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors_list.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<IDictionary<string, string>> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
IGraphNodeBase batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
IGraphNodeBase min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
Nullable<int> seed
Seed for the random shuffling within the queue.
bool enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectshuffle_batch_join(IEnumerable<IDictionary<string, string>> tensors_list, IGraphNodeBase batch_size, int capacity, IGraphNodeBase min_after_dequeue, Nullable<int> seed, Nullable<int> enqueue_many, object shapes, bool allow_smaller_final_batch, string shared_name, string name)

Create batches by randomly shuffling tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).shuffle(min_after_dequeue).batch(batch_size).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.shuffle_batch().

This version enqueues a different list of tensors in different threads. It adds the following to the current Graph:

* A shuffling queue into which tensors from tensors_list are enqueued. * A dequeue_many operation to create batches from the queue. * A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors_list.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<IDictionary<string, string>> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
IGraphNodeBase batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
IGraphNodeBase min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
Nullable<int> seed
Seed for the random shuffling within the queue.
Nullable<int> enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectshuffle_batch_join(IEnumerable<IDictionary<string, string>> tensors_list, int batch_size, int capacity, IGraphNodeBase min_after_dequeue, Nullable<int> seed, Nullable<int> enqueue_many, object shapes, bool allow_smaller_final_batch, string shared_name, string name)

Create batches by randomly shuffling tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).shuffle(min_after_dequeue).batch(batch_size).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.shuffle_batch().

This version enqueues a different list of tensors in different threads. It adds the following to the current Graph:

* A shuffling queue into which tensors from tensors_list are enqueued. * A dequeue_many operation to create batches from the queue. * A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors_list.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
IEnumerable<IDictionary<string, string>> tensors_list
A list of tuples or dictionaries of tensors to enqueue.
int batch_size
An integer. The new batch size pulled from the queue.
int capacity
An integer. The maximum number of elements in the queue.
IGraphNodeBase min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
Nullable<int> seed
Seed for the random shuffling within the queue.
Nullable<int> enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
bool allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
string shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
string name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### objectshuffle_batch_join_dyn(object tensors_list, object batch_size, object capacity, object min_after_dequeue, object seed, ImplicitContainer<T> enqueue_many, object shapes, ImplicitContainer<T> allow_smaller_final_batch, object shared_name, object name)

Create batches by randomly shuffling tensors. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.interleave(...).shuffle(min_after_dequeue).batch(batch_size).

The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.compat.v1.train.shuffle_batch().

This version enqueues a different list of tensors in different threads. It adds the following to the current Graph:

* A shuffling queue into which tensors from tensors_list are enqueued. * A dequeue_many operation to create batches from the queue. * A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors_list.

len(tensors_list) threads will be started, with thread i enqueuing the tensors from tensors_list[i]. tensors_list[i1][j] must match tensors_list[i2][j] in type and shape, except in the first dimension if enqueue_many is true.

If enqueue_many is False, each tensors_list[i] is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors_list[i] is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors_list[i] should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z].

The capacity argument controls the how long the prefetching is allowed to grow the queues.

The returned operation is a dequeue operation and will throw tf.errors.OutOfRangeError if the input queue is exhausted. If this operation is feeding another input queue, its queue runner will catch this exception, however, if this operation is used in your main thread you are responsible for catching this yourself.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queue is closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the shape property will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.
##### Parameters
object tensors_list
A list of tuples or dictionaries of tensors to enqueue.
object batch_size
An integer. The new batch size pulled from the queue.
object capacity
An integer. The maximum number of elements in the queue.
object min_after_dequeue
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
object seed
Seed for the random shuffling within the queue.
ImplicitContainer<T> enqueue_many
Whether each tensor in tensor_list_list is a single example.
object shapes
(Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
ImplicitContainer<T> allow_smaller_final_batch
(Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue.
object shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
object name
(Optional) A name for the operations.
##### Returns
object
A list or dictionary of tensors with the same number and types as tensors_list[i].

#### IList<Tensor>slice_input_producer(IEnumerable<IGraphNodeBase> tensor_list, Nullable<int> num_epochs, bool shuffle, Nullable<int> seed, int capacity, string shared_name, string name)

Produces a slice of each Tensor in tensor_list. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(tuple(tensor_list)).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Implemented using a Queue -- a QueueRunner for the Queue is added to the current Graph's QUEUE_RUNNER collection.
##### Parameters
IEnumerable<IGraphNodeBase> tensor_list
A list of Tensor objects. Every Tensor in tensor_list must have the same size in the first dimension.
Nullable<int> num_epochs
An integer (optional). If specified, slice_input_producer produces each slice num_epochs times before generating an OutOfRange error. If not specified, slice_input_producer can cycle through the slices an unlimited number of times.
bool shuffle
Boolean. If true, the integers are randomly shuffled within each epoch.
Nullable<int> seed
An integer (optional). Seed used if shuffle == True.
int capacity
An integer. Sets the queue capacity.
string shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
string name
A name for the operations (optional).
##### Returns
IList<Tensor>
A list of tensors, one for each element of tensor_list. If the tensor in tensor_list has shape [N, a, b,.., z], then the corresponding output tensor will have shape [a, b,..., z].

#### objectslice_input_producer_dyn(object tensor_list, object num_epochs, ImplicitContainer<T> shuffle, object seed, ImplicitContainer<T> capacity, object shared_name, object name)

Produces a slice of each Tensor in tensor_list. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(tuple(tensor_list)).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Implemented using a Queue -- a QueueRunner for the Queue is added to the current Graph's QUEUE_RUNNER collection.
##### Parameters
object tensor_list
A list of Tensor objects. Every Tensor in tensor_list must have the same size in the first dimension.
object num_epochs
An integer (optional). If specified, slice_input_producer produces each slice num_epochs times before generating an OutOfRange error. If not specified, slice_input_producer can cycle through the slices an unlimited number of times.
ImplicitContainer<T> shuffle
Boolean. If true, the integers are randomly shuffled within each epoch.
object seed
An integer (optional). Seed used if shuffle == True.
ImplicitContainer<T> capacity
An integer. Sets the queue capacity.
object shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions.
object name
A name for the operations (optional).
##### Returns
object
A list of tensors, one for each element of tensor_list. If the tensor in tensor_list has shape [N, a, b,.., z], then the corresponding output tensor will have shape [a, b,..., z].

#### IList<object>start_queue_runners(MonitoredSession sess, Coordinator coord, bool daemon, bool start, ImplicitContainer<T> collection)

Starts all queue runners collected in the graph. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: To construct input pipelines, use the tf.data module.

This is a companion method to add_queue_runner(). It just starts threads for all queue runners collected in the graph. It returns the list of all threads.
##### Parameters
MonitoredSession sess
Session used to run the queue ops. Defaults to the default session.
Coordinator coord
Optional Coordinator for coordinating the started threads.
bool daemon
Whether the threads should be marked as daemons, meaning they don't block program exit.
bool start
Set to False to only create the threads, not start them.
ImplicitContainer<T> collection
A GraphKey specifying the graph collection to get the queue runners from. Defaults to GraphKeys.QUEUE_RUNNERS.
IList<object>

#### IList<object>start_queue_runners(string sess, Coordinator coord, bool daemon, bool start, ImplicitContainer<T> collection)

Starts all queue runners collected in the graph. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: To construct input pipelines, use the tf.data module.

This is a companion method to add_queue_runner(). It just starts threads for all queue runners collected in the graph. It returns the list of all threads.
##### Parameters
string sess
Session used to run the queue ops. Defaults to the default session.
Coordinator coord
Optional Coordinator for coordinating the started threads.
bool daemon
Whether the threads should be marked as daemons, meaning they don't block program exit.
bool start
Set to False to only create the threads, not start them.
ImplicitContainer<T> collection
A GraphKey specifying the graph collection to get the queue runners from. Defaults to GraphKeys.QUEUE_RUNNERS.
IList<object>

#### IList<object>start_queue_runners(Session sess, Coordinator coord, bool daemon, bool start, ImplicitContainer<T> collection)

Starts all queue runners collected in the graph. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: To construct input pipelines, use the tf.data module.

This is a companion method to add_queue_runner(). It just starts threads for all queue runners collected in the graph. It returns the list of all threads.
##### Parameters
Session sess
Session used to run the queue ops. Defaults to the default session.
Coordinator coord
Optional Coordinator for coordinating the started threads.
bool daemon
Whether the threads should be marked as daemons, meaning they don't block program exit.
bool start
Set to False to only create the threads, not start them.
ImplicitContainer<T> collection
A GraphKey specifying the graph collection to get the queue runners from. Defaults to GraphKeys.QUEUE_RUNNERS.
IList<object>

#### IList<object>start_queue_runners(_CoordinatedSession sess, Coordinator coord, bool daemon, bool start, ImplicitContainer<T> collection)

Starts all queue runners collected in the graph. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: To construct input pipelines, use the tf.data module.

This is a companion method to add_queue_runner(). It just starts threads for all queue runners collected in the graph. It returns the list of all threads.
##### Parameters
_CoordinatedSession sess
Session used to run the queue ops. Defaults to the default session.
Coordinator coord
Optional Coordinator for coordinating the started threads.
bool daemon
Whether the threads should be marked as daemons, meaning they don't block program exit.
bool start
Set to False to only create the threads, not start them.
ImplicitContainer<T> collection
A GraphKey specifying the graph collection to get the queue runners from. Defaults to GraphKeys.QUEUE_RUNNERS.
IList<object>

#### IList<object>start_queue_runners(LocalCLIDebugWrapperSession sess, Coordinator coord, bool daemon, bool start, ImplicitContainer<T> collection)

Starts all queue runners collected in the graph. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: To construct input pipelines, use the tf.data module.

This is a companion method to add_queue_runner(). It just starts threads for all queue runners collected in the graph. It returns the list of all threads.
##### Parameters
LocalCLIDebugWrapperSession sess
Session used to run the queue ops. Defaults to the default session.
Coordinator coord
Optional Coordinator for coordinating the started threads.
bool daemon
Whether the threads should be marked as daemons, meaning they don't block program exit.
bool start
Set to False to only create the threads, not start them.
ImplicitContainer<T> collection
A GraphKey specifying the graph collection to get the queue runners from. Defaults to GraphKeys.QUEUE_RUNNERS.
IList<object>

#### objectstart_queue_runners_dyn(object sess, object coord, ImplicitContainer<T> daemon, ImplicitContainer<T> start, ImplicitContainer<T> collection)

Starts all queue runners collected in the graph. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: To construct input pipelines, use the tf.data module.

This is a companion method to add_queue_runner(). It just starts threads for all queue runners collected in the graph. It returns the list of all threads.
##### Parameters
object sess
Session used to run the queue ops. Defaults to the default session.
object coord
Optional Coordinator for coordinating the started threads.
ImplicitContainer<T> daemon
Whether the threads should be marked as daemons, meaning they don't block program exit.
ImplicitContainer<T> start
Set to False to only create the threads, not start them.
ImplicitContainer<T> collection
A GraphKey specifying the graph collection to get the queue runners from. Defaults to GraphKeys.QUEUE_RUNNERS.
object

#### objectstring_input_producer(IGraphNodeBase string_tensor, Nullable<int> num_epochs, bool shuffle, Nullable<int> seed, int capacity, string shared_name, string name, object cancel_op)

Output strings (e.g. filenames) to a queue for an input pipeline. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Note: if num_epochs is not None, this function creates local counter epochs. Use local_variables_initializer() to initialize local variables.
##### Parameters
IGraphNodeBase string_tensor
A 1-D string tensor with the strings to produce.
Nullable<int> num_epochs
An integer (optional). If specified, string_input_producer produces each string from string_tensor num_epochs times before generating an OutOfRange error. If not specified, string_input_producer can cycle through the strings in string_tensor an unlimited number of times.
bool shuffle
Boolean. If true, the strings are randomly shuffled within each epoch.
Nullable<int> seed
An integer (optional). Seed used if shuffle == True.
int capacity
An integer. Sets the queue capacity.
string shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions. All sessions open to the device which has this queue will be able to access it via the shared_name. Using this in a distributed setting means each name will only be seen by one of the sessions which has access to this operation.
string name
A name for the operations (optional).
object cancel_op
Cancel op for the queue (optional).
##### Returns
object
A queue with the output strings. A QueueRunner for the Queue is added to the current Graph's QUEUE_RUNNER collection.

#### objectstring_input_producer(IEnumerable<object> string_tensor, Nullable<int> num_epochs, bool shuffle, Nullable<int> seed, int capacity, string shared_name, string name, object cancel_op)

Output strings (e.g. filenames) to a queue for an input pipeline. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Note: if num_epochs is not None, this function creates local counter epochs. Use local_variables_initializer() to initialize local variables.
##### Parameters
IEnumerable<object> string_tensor
A 1-D string tensor with the strings to produce.
Nullable<int> num_epochs
An integer (optional). If specified, string_input_producer produces each string from string_tensor num_epochs times before generating an OutOfRange error. If not specified, string_input_producer can cycle through the strings in string_tensor an unlimited number of times.
bool shuffle
Boolean. If true, the strings are randomly shuffled within each epoch.
Nullable<int> seed
An integer (optional). Seed used if shuffle == True.
int capacity
An integer. Sets the queue capacity.
string shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions. All sessions open to the device which has this queue will be able to access it via the shared_name. Using this in a distributed setting means each name will only be seen by one of the sessions which has access to this operation.
string name
A name for the operations (optional).
object cancel_op
Cancel op for the queue (optional).
##### Returns
object
A queue with the output strings. A QueueRunner for the Queue is added to the current Graph's QUEUE_RUNNER collection.

#### objectstring_input_producer_dyn(object string_tensor, object num_epochs, ImplicitContainer<T> shuffle, object seed, ImplicitContainer<T> capacity, object shared_name, object name, object cancel_op)

Output strings (e.g. filenames) to a queue for an input pipeline. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Note: if num_epochs is not None, this function creates local counter epochs. Use local_variables_initializer() to initialize local variables.
##### Parameters
object string_tensor
A 1-D string tensor with the strings to produce.
object num_epochs
An integer (optional). If specified, string_input_producer produces each string from string_tensor num_epochs times before generating an OutOfRange error. If not specified, string_input_producer can cycle through the strings in string_tensor an unlimited number of times.
ImplicitContainer<T> shuffle
Boolean. If true, the strings are randomly shuffled within each epoch.
object seed
An integer (optional). Seed used if shuffle == True.
ImplicitContainer<T> capacity
An integer. Sets the queue capacity.
object shared_name
(optional). If set, this queue will be shared under the given name across multiple sessions. All sessions open to the device which has this queue will be able to access it via the shared_name. Using this in a distributed setting means each name will only be seen by one of the sessions which has access to this operation.
object name
A name for the operations (optional).
object cancel_op
Cancel op for the queue (optional).
##### Returns
object
A queue with the output strings. A QueueRunner for the Queue is added to the current Graph's QUEUE_RUNNER collection.

#### IEnumerator<object>summary_iterator(string path)

An iterator for reading Event protocol buffers from an event file.

You can use this function to read events written to an event file. It returns a Python iterator that yields Event protocol buffers.

Example: Print the contents of an events file. Example: Print selected summary values. See the protocol buffer definitions of [Event](https://www.tensorflow.org/code/tensorflow/core/util/event.proto) and [Summary](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) for more information about their attributes.
##### Parameters
string path
The path to an event file created by a SummaryWriter.
Show Example
for e in tf.compat.v1.train.summary_iterator(path to events file):
print(e)

#### IEnumerator<object>summary_iterator(IEnumerable<object> path)

An iterator for reading Event protocol buffers from an event file.

You can use this function to read events written to an event file. It returns a Python iterator that yields Event protocol buffers.

Example: Print the contents of an events file. Example: Print selected summary values. See the protocol buffer definitions of [Event](https://www.tensorflow.org/code/tensorflow/core/util/event.proto) and [Summary](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) for more information about their attributes.
##### Parameters
IEnumerable<object> path
The path to an event file created by a SummaryWriter.
Show Example
for e in tf.compat.v1.train.summary_iterator(path to events file):
print(e)

#### objectsummary_iterator_dyn(object path)

An iterator for reading Event protocol buffers from an event file.

You can use this function to read events written to an event file. It returns a Python iterator that yields Event protocol buffers.

Example: Print the contents of an events file. Example: Print selected summary values. See the protocol buffer definitions of [Event](https://www.tensorflow.org/code/tensorflow/core/util/event.proto) and [Summary](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) for more information about their attributes.
##### Parameters
object path
The path to an event file created by a SummaryWriter.
Show Example
for e in tf.compat.v1.train.summary_iterator(path to events file):
print(e)

#### voidupdate_checkpoint_state(string save_dir, object model_checkpoint_path, IEnumerable<object> all_model_checkpoint_paths, object latest_filename, object all_model_checkpoint_timestamps, object last_preserved_timestamp)

Updates the content of the 'checkpoint' file. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.train.CheckpointManager to manage checkpoints rather than manually editing the Checkpoint proto.

This updates the checkpoint file containing a CheckpointState proto.
##### Parameters
string save_dir
Directory where the model was saved.
object model_checkpoint_path
The checkpoint file.
IEnumerable<object> all_model_checkpoint_paths
List of strings. Paths to all not-yet-deleted checkpoints, sorted from oldest to newest. If this is a non-empty list, the last element must be equal to model_checkpoint_path. These paths are also saved in the CheckpointState proto.
object latest_filename
Optional name of the checkpoint file. Default to 'checkpoint'.
object all_model_checkpoint_timestamps
Optional list of timestamps (floats, seconds since the Epoch) indicating when the checkpoints in all_model_checkpoint_paths were created.
object last_preserved_timestamp
A float, indicating the number of seconds since the Epoch when the last preserved checkpoint was written, e.g. due to a keep_checkpoint_every_n_hours parameter (see tf.contrib.checkpoint.CheckpointManager for an implementation).

#### objectupdate_checkpoint_state_dyn(object save_dir, object model_checkpoint_path, object all_model_checkpoint_paths, object latest_filename, object all_model_checkpoint_timestamps, object last_preserved_timestamp)

Updates the content of the 'checkpoint' file. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.train.CheckpointManager to manage checkpoints rather than manually editing the Checkpoint proto.

This updates the checkpoint file containing a CheckpointState proto.
##### Parameters
object save_dir
Directory where the model was saved.
object model_checkpoint_path
The checkpoint file.
object all_model_checkpoint_paths
List of strings. Paths to all not-yet-deleted checkpoints, sorted from oldest to newest. If this is a non-empty list, the last element must be equal to model_checkpoint_path. These paths are also saved in the CheckpointState proto.
object latest_filename
Optional name of the checkpoint file. Default to 'checkpoint'.
object all_model_checkpoint_timestamps
Optional list of timestamps (floats, seconds since the Epoch) indicating when the checkpoints in all_model_checkpoint_paths were created.
object last_preserved_timestamp
A float, indicating the number of seconds since the Epoch when the last preserved checkpoint was written, e.g. due to a keep_checkpoint_every_n_hours parameter (see tf.contrib.checkpoint.CheckpointManager for an implementation).

#### voidwarm_start(string ckpt_to_initialize_from, IEnumerable<string> vars_to_warm_start, IDictionary<object, object> var_name_to_vocab_info, IDictionary<object, object> var_name_to_prev_var_name)

Warm-starts a model using the given settings.

If you are using a tf.estimator.Estimator, this will automatically be called during training.
##### Parameters
string ckpt_to_initialize_from
[Required] A string specifying the directory with checkpoint file(s) or path to checkpoint from which to warm-start the model parameters.
IEnumerable<string> vars_to_warm_start
[Optional] One of the following:

- A regular expression (string) that captures which variables to warm-start (see tf.compat.v1.get_collection). This expression will only consider variables in the TRAINABLE_VARIABLES collection -- if you need to warm-start non_TRAINABLE vars (such as optimizer accumulators or batch norm statistics), please use the below option. - A list of strings, each a regex scope provided to tf.compat.v1.get_collection with GLOBAL_VARIABLES (please see tf.compat.v1.get_collection). For backwards compatibility reasons, this is separate from the single-string argument type. - A list of Variables to warm-start. If you do not have access to the Variable objects at the call site, please use the above option. - None, in which case only TRAINABLE variables specified in var_name_to_vocab_info will be warm-started.

Defaults to '.*', which warm-starts all variables in the TRAINABLE_VARIABLES collection. Note that this excludes variables such as accumulators and moving statistics from batch norm.
IDictionary<object, object> var_name_to_vocab_info
[Optional] Dict of variable names (strings) to tf.estimator.VocabInfo. The variable names should be "full" variables, not the names of the partitions. If not explicitly provided, the variable is assumed to have no (changes to) vocabulary.
IDictionary<object, object> var_name_to_prev_var_name
[Optional] Dict of variable names (strings) to name of the previously-trained variable in ckpt_to_initialize_from. If not explicitly provided, the name of the variable is assumed to be same between previous checkpoint and current model. Note that this has no effect on the set of variables that is warm-started, and only controls name mapping (use vars_to_warm_start for controlling what variables to warm-start).

#### voidwarm_start(string ckpt_to_initialize_from, string vars_to_warm_start, IDictionary<object, object> var_name_to_vocab_info, IDictionary<object, object> var_name_to_prev_var_name)

Warm-starts a model using the given settings.

If you are using a tf.estimator.Estimator, this will automatically be called during training.
##### Parameters
string ckpt_to_initialize_from
[Required] A string specifying the directory with checkpoint file(s) or path to checkpoint from which to warm-start the model parameters.
string vars_to_warm_start
[Optional] One of the following:

- A regular expression (string) that captures which variables to warm-start (see tf.compat.v1.get_collection). This expression will only consider variables in the TRAINABLE_VARIABLES collection -- if you need to warm-start non_TRAINABLE vars (such as optimizer accumulators or batch norm statistics), please use the below option. - A list of strings, each a regex scope provided to tf.compat.v1.get_collection with GLOBAL_VARIABLES (please see tf.compat.v1.get_collection). For backwards compatibility reasons, this is separate from the single-string argument type. - A list of Variables to warm-start. If you do not have access to the Variable objects at the call site, please use the above option. - None, in which case only TRAINABLE variables specified in var_name_to_vocab_info will be warm-started.

Defaults to '.*', which warm-starts all variables in the TRAINABLE_VARIABLES collection. Note that this excludes variables such as accumulators and moving statistics from batch norm.
IDictionary<object, object> var_name_to_vocab_info
[Optional] Dict of variable names (strings) to tf.estimator.VocabInfo. The variable names should be "full" variables, not the names of the partitions. If not explicitly provided, the variable is assumed to have no (changes to) vocabulary.
IDictionary<object, object> var_name_to_prev_var_name
[Optional] Dict of variable names (strings) to name of the previously-trained variable in ckpt_to_initialize_from. If not explicitly provided, the name of the variable is assumed to be same between previous checkpoint and current model. Note that this has no effect on the set of variables that is warm-started, and only controls name mapping (use vars_to_warm_start for controlling what variables to warm-start).

#### objectwarm_start_dyn(object ckpt_to_initialize_from, ImplicitContainer<T> vars_to_warm_start, object var_name_to_vocab_info, object var_name_to_prev_var_name)

Warm-starts a model using the given settings.

If you are using a tf.estimator.Estimator, this will automatically be called during training.
##### Parameters
object ckpt_to_initialize_from
[Required] A string specifying the directory with checkpoint file(s) or path to checkpoint from which to warm-start the model parameters.
ImplicitContainer<T> vars_to_warm_start
[Optional] One of the following:

- A regular expression (string) that captures which variables to warm-start (see tf.compat.v1.get_collection). This expression will only consider variables in the TRAINABLE_VARIABLES collection -- if you need to warm-start non_TRAINABLE vars (such as optimizer accumulators or batch norm statistics), please use the below option. - A list of strings, each a regex scope provided to tf.compat.v1.get_collection with GLOBAL_VARIABLES (please see tf.compat.v1.get_collection). For backwards compatibility reasons, this is separate from the single-string argument type. - A list of Variables to warm-start. If you do not have access to the Variable objects at the call site, please use the above option. - None, in which case only TRAINABLE variables specified in var_name_to_vocab_info will be warm-started.

Defaults to '.*', which warm-starts all variables in the TRAINABLE_VARIABLES collection. Note that this excludes variables such as accumulators and moving statistics from batch norm.
object var_name_to_vocab_info
[Optional] Dict of variable names (strings) to tf.estimator.VocabInfo. The variable names should be "full" variables, not the names of the partitions. If not explicitly provided, the variable is assumed to have no (changes to) vocabulary.
object var_name_to_prev_var_name
[Optional] Dict of variable names (strings) to name of the previously-trained variable in ckpt_to_initialize_from. If not explicitly provided, the name of the variable is assumed to be same between previous checkpoint and current model. Note that this has no effect on the set of variables that is warm-started, and only controls name mapping (use vars_to_warm_start for controlling what variables to warm-start).