tf.contrib.training.stratified_sample

Stochastically creates batches based on per-class probabilities.

tf.contrib.training.stratified_sample(
    tensors, labels, target_probs, batch_size, init_probs=None, enqueue_many=False,
    queue_capacity=16, threads_per_queue=1, name=None
)

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.

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.
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.

Raises
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).

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.

target_probs = [...distribution you want...] [data_batch], labels = tf.contrib.training.stratified_sample( [data], label, target_probs)

Run batch through network.

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