tf.train.batch( tensors, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None )
Creates batches of tensors in
tensors can be a list or a dictionary of tensors.
The value returned by the function will be of the same type
This function is implemented using a queue. A
QueueRunner for the
queue is added to the current
tensors is assumed to represent a single
example. An input tensor with shape
[x, y, z] will be output as a tensor
[batch_size, x, y, z].
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
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.
False, you must ensure that either
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.
True, it is sufficient that the rank of the
tensors is known, but individual dimensions may have shape
In this case, for each enqueue the dimensions with value
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.
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
operations that depend on fixed batch_size would fail.
tensors: The list or dictionary of tensors to enqueue.
batch_size: The new batch size pulled from the queue.
num_threads: The number of threads enqueuing
tensors. The batching will be nondeterministic if
num_threads > 1.
capacity: An integer. The maximum number of elements in the queue.
enqueue_many: Whether each tensor in
tensorsis a single example.
shapes: (Optional) The shapes for each example. Defaults to the inferred shapes for
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.
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).
ValueError: If the
shapesare not specified, and cannot be inferred from the elements of
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.