tf.contrib.training.stratified_sample()

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)

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. See stratified_sample_unknown_dist for a function that performs stratified sampling with one queue per class and doesn't require knowing the class data-distribution 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.
• 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: 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. ...