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)

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)

See the guide: Inputs and Readers > Input pipeline

Creates batches of tensors in tensors.

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 get_shape method will have a first Dimension value of None, and operations that depend on fixed batch_size would fail.

Note: if num_epochs is not None, this function creates local counter epochs. Use local_variables_initializer() to initialize local variables.

Args:

• 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.
• capacity: An integer. The maximum number of elements in the queue.
• enqueue_many: Whether each tensor in tensors is a single example.
• shapes: (Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
• 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 types as tensors (except if the input is a list of one element, then it returns a tensor, not a list).

Raises:

• ValueError: If the shapes are not specified, and cannot be inferred from the elements of tensors.

Defined in tensorflow/python/training/input.py.