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Stochastically creates batches by rejection sampling.

Each list of non-batched tensors is evaluated by accept_prob_fn, to produce a scalar tensor between 0 and 1. This tensor corresponds to the probability of being accepted. When batch_size tensor groups have been accepted, the batch queue will return a mini-batch.


  • tensors: List of tensors for data. All tensors are either one item or a batch, according to enqueue_many.
  • accept_prob_fn: A python lambda that takes a non-batch tensor from each item in tensors, and produces a scalar tensor.
  • batch_size: Size of batch to be returned.
  • queue_threads: The number of threads for the queue that will hold the final batch.
  • enqueue_many: Bool. If true, interpret input tensors as having a batch dimension.
  • prebatch_capacity: Capacity for the large queue that is used to convert batched tensors to single examples.
  • prebatch_threads: Number of threads for the large queue that is used to convert batched tensors to single examples.
  • runtime_checks: Bool. If true, insert runtime checks on the output of accept_prob_fn. Using True might have a performance impact.
  • name: Optional prefix for ops created by this function.


  • ValueError: enqueue_many is True and labels doesn't have a batch dimension, or if enqueue_many is False and labels isn't a scalar.
  • ValueError: enqueue_many is True, and batch dimension on data and labels don't match.
  • ValueError: if a zero initial probability class has a nonzero target probability.


A list of tensors of the same length as tensors, with batch dimension batch_size.


Get tensor for a single data and label example.

data, label = data_provider.Get(['data', 'label'])

Get stratified batch according to data tensor.

accept_prob_fn = lambda x: (tf.tanh(x[0]) + 1) / 2 data_batch = [data, label], accept_prob_fn, 16)

Run batch through network.