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tf.contrib.training.rejection_sample

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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.

Example:

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 = tf.contrib.training.rejection_sample( [data, label], accept_prob_fn, 16)

Run batch through network.

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