tf.random.all_candidate_sampler

Aliases:

• tf.nn.all_candidate_sampler
• tf.random.all_candidate_sampler

tf.random.all_candidate_sampler(
    true_classes,
    num_true,
    num_sampled,
    unique,
    seed=None,
    name=None
)

Defined in tensorflow/python/ops/candidate_sampling_ops.py.

Generate the set of all classes.

Deterministically generates and returns the set of all possible classes. For testing purposes. There is no need to use this, since you might as well use full softmax or full logistic regression.

Args:

• true_classes: A Tensor of type int64 and shape [batch_size, num_true]. The target classes.
• num_true: An int. The number of target classes per training example.
• num_sampled: An int. The number of possible classes.
• unique: A bool. Ignored. unique.
• seed: An int. An operation-specific seed. Default is 0.
• name: A name for the operation (optional).

Returns:

• sampled_candidates: A tensor of type int64 and shape [num_sampled]. This operation deterministically returns the entire range [0, num_sampled].
• true_expected_count: A tensor of type float. Same shape as true_classes. The expected counts under the sampling distribution of each of true_classes. All returned values are 1.0.
• sampled_expected_count: A tensor of type float. Same shape as sampled_candidates. The expected counts under the sampling distribution of each of sampled_candidates. All returned values are 1.0.