tf.contrib.training.rejection_sample( tensors, accept_prob_fn, batch_size, queue_threads=1, enqueue_many=False, prebatch_capacity=16, prebatch_threads=1, runtime_checks=False, name=None )
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
Truemight 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
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) + 1) / 2 data_batch = tf.contrib.training.rejection_sample( [data, label], accept_prob_fn, 16)
# Run batch through network. ...