View source on GitHub |
Creates batches by randomly shuffling tensors. (deprecated)
tf.compat.v1.train.shuffle_batch(
tensors,
batch_size,
capacity,
min_after_dequeue,
num_threads=1,
seed=None,
enqueue_many=False,
shapes=None,
allow_smaller_final_batch=False,
shared_name=None,
name=None
)
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
toQUEUE_RUNNER
collection, to enqueue the tensors fromtensors
.
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.
For 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,
num_threads=4,
capacity=50000,
min_after_dequeue=10000)
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.
Returns | |
---|---|
A list or dictionary of tensors with the types as tensors .
|
Raises | |
---|---|
ValueError
|
If the shapes are not specified, and cannot be
inferred from the elements of tensors .
|
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.