tf.contrib.training.stratified_sample_unknown_dist()

tf.contrib.training.stratified_sample_unknown_dist(tensors, labels, probs, batch_size, enqueue_many=False, queue_capacity=16, threads_per_queue=1, name=None)

Stochastically creates batches based on per-class probabilities.

NOTICE This sampler can be significantly slower than stratified_sample due to each thread discarding all examples not in its assigned class.

This uses a number of threads proportional to the number of classes. See stratified_sample for an implementation that discards fewer examples and uses a fixed number of threads. This function's only advantage over stratified_sample is that the class data-distribution doesn't need to be known ahead of time.

Args:

• tensors: List of tensors for data. All tensors are either one item or a batch, according to enqueue_many.
• labels: Tensor for label of data. Label is a single integer or a batch, depending on enqueue_many. It is not a one-hot vector.
• probs: Target class probabilities. An object whose type has a registered Tensor conversion function.
• batch_size: Size of batch to be returned.
• enqueue_many: Bool. If true, interpret input tensors as having a batch dimension.
• queue_capacity: Capacity of each per-class queue.
• threads_per_queue: Number of threads for each per-class queue.
• 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 of data and labels don't match.
• ValueError: if probs don't sum to one.
• TFAssertion: if labels aren't integers in [0, num classes).

Returns:

(data_batch, label_batch), where data_batch is a list of tensors of the same length as tensors

Example:

# Get tensor for a single data and label example. data, label = data_provider.Get(['data', 'label'])

# Get stratified batch according to per-class probabilities. init_probs = [1.0/NUM_CLASSES for _ in range(NUM_CLASSES)] [data_batch], labels = ( tf.contrib.training.stratified_sample_unknown_dist( [data], label, init_probs, 16))

# Run batch through network. ...