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Stochastically creates batches based on per-class probabilities.

This method discards examples. Internally, it creates one queue to amortize the cost of disk reads, and one queue to hold the properly-proportioned batch.

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.
target_probs Target class proportions in batch. An object whose type has a registered Tensor conversion function.
batch_size Size of batch to be returned.
init_probs Class proportions in the data. An object whose type has a registered Tensor conversion function, or None for estimating the initial distribution.
enqueue_many Bool. If true, interpret input tensors as having a batch dimension.
queue_capacity Capacity of the large queue that holds input examples.
threads_per_queue Number of threads for the large queue that holds input examples and for the final queue with the proper class proportions.
name Optional prefix for ops created by this function.

ValueError If tensors isn't iterable.
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 probs don't sum to one.
ValueError if a zero initial probability class has a nonzero target probability.
TFAssertion if labels aren't integers in [0, num classes).

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


Get tensor for a single data and label example.

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

Get stratified batch according to per-class probabilities.

target_probs = [...distribution you want...] [data_batch], labels = [data], label, target_probs)

Run batch through network.