tf.contrib.training.rejection_sample

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
)

Defined in tensorflow/contrib/training/python/training/sampling_ops.py.

See the guide: Training (contrib) > Online data resampling

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.

Args:

• 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.

Raises:

• 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.

Returns:

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. ...