tf.train.maybe_batch

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Conditionally creates batches of tensors based on keep_input. (deprecated)

tf.train.maybe_batch(
    tensors, keep_input, 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 docstring in batch for more details.

tensors The list or dictionary of tensors to enqueue.
keep_input A bool Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates True, then tensors are all added to the queue. If it is a vector and enqueue_many is True, then each example is added to the queue only if the corresponding value in keep_input is True. This tensor essentially acts as a filtering mechanism.
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 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.

A list or dictionary of tensors with the same types as tensors.

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