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Create batches by randomly shuffling tensors. (deprecated)
tf.compat.v1.train.shuffle_batch_join(
tensors_list, batch_size, capacity, min_after_dequeue, seed=None,
enqueue_many=False, shapes=None, allow_smaller_final_batch=False,
shared_name=None, name=None
)
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
toQUEUE_RUNNER
collection, to enqueue the tensors fromtensors_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.
Args | |
---|---|
tensors_list
|
A list of tuples or dictionaries of tensors to enqueue. |
batch_size
|
An integer. The new batch size pulled from the queue. |
capacity
|
An integer. The maximum number of elements in the queue. |
min_after_dequeue
|
Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements. |
seed
|
Seed for the random shuffling within the queue. |
enqueue_many
|
Whether each tensor in tensor_list_list is a single
example.
|
shapes
|
(Optional) The shapes for each example. Defaults to the
inferred shapes for tensors_list[i] .
|
allow_smaller_final_batch
|
(Optional) Boolean. If True , allow the final
batch to be smaller if there are insufficient items left in the queue.
|
shared_name
|
(optional). If set, this queue will be shared under the given name across multiple sessions. |
name
|
(Optional) A name for the operations. |
Returns | |
---|---|
A list or dictionary of tensors with the same number and types as
tensors_list[i] .
|
Raises | |
---|---|
ValueError
|
If the shapes are not specified, and cannot be
inferred from the elements of tensors_list .
|
Eager Compatibility
Input pipelines based on Queues are not supported when eager execution is
enabled. Please use the tf.data
API to ingest data under eager execution.