tf.random.all_candidate_sampler

Generate the set of all classes.

Main aliases

tf.nn.all_candidate_sampler

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.nn.all_candidate_sampler, tf.compat.v1.random.all_candidate_sampler

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

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.

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).

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.