tf.contrib.nn.sampled_sparse_softmax_loss

tf.contrib.nn.sampled_sparse_softmax_loss(
    weights,
    biases,
    labels,
    inputs,
    num_sampled,
    num_classes,
    sampled_values=None,
    remove_accidental_hits=True,
    partition_strategy='mod',
    name='sampled_sparse_softmax_loss'
)

Defined in tensorflow/contrib/nn/python/ops/sampling_ops.py.

Computes and returns the sampled sparse softmax training loss.

This is a faster way to train a softmax classifier over a huge number of classes.

This operation is for training only. It is generally an underestimate of the full softmax loss.

A common use case is to use this method for training, and calculate the full softmax loss for evaluation or inference. In this case, you must set partition_strategy="div" for the two losses to be consistent, as in the following example:

if mode == "train":
  loss = tf.nn.sampled_sparse_softmax_loss(
      weights=weights,
      biases=biases,
      labels=labels,
      inputs=inputs,
      ...,
      partition_strategy="div")
elif mode == "eval":
  logits = tf.matmul(inputs, tf.transpose(weights))
  logits = tf.nn.bias_add(logits, biases)
  loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
      labels=tf.squeeze(labels),
      logits=logits)

See our Candidate Sampling Algorithms Reference

Also see Section 3 of Jean et al., 2014 (pdf) for the math.

Args:

• weights: A Tensor of shape [num_classes, dim], or a list of Tensor objects whose concatenation along dimension 0 has shape [num_classes, dim]. The (possibly-sharded) class embeddings.
• biases: A Tensor of shape [num_classes]. The class biases.
• labels: A Tensor of type int64 and shape [batch_size, 1]. The index of the single target class for each row of logits. Note that this format differs from the labels argument of nn.sparse_softmax_cross_entropy_with_logits.
• inputs: A Tensor of shape [batch_size, dim]. The forward activations of the input network.
• num_sampled: An int. The number of classes to randomly sample per batch.
• num_classes: An int. The number of possible classes.
• sampled_values: a tuple of (sampled_candidates, true_expected_count, sampled_expected_count) returned by a *_candidate_sampler function. (if None, we default to log_uniform_candidate_sampler)
• remove_accidental_hits: A bool. whether to remove "accidental hits" where a sampled class equals one of the target classes. Default is True.
• partition_strategy: A string specifying the partitioning strategy, relevant if len(weights) > 1. Currently "div" and "mod" are supported. Default is "mod". See tf.nn.embedding_lookup for more details.
• name: A name for the operation (optional).

Returns:

A batch_size 1-D tensor of per-example sampled softmax losses.