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Runs a list of tensors to fill a queue to create batches of examples. (deprecated)
tf.compat.v1.train.batch_join(
tensors_list, batch_size, capacity=32, enqueue_many=False, shapes=None,
dynamic_pad=False, 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.batch().
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.
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.
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. |
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 tensor_list_list[i].
|
dynamic_pad
|
Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes. |
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 tensor_list_list.
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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.