tf.nn.sampled_softmax_loss( weights, biases, labels, inputs, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=True, partition_strategy='mod', name='sampled_softmax_loss', seed=None )
Defined in tensorflow/python/ops/nn_impl.py
.
See the guide: Neural Network > Candidate Sampling
Computes and returns the sampled 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_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) labels_one_hot = tf.one_hot(labels, n_classes) loss = tf.nn.softmax_cross_entropy_with_logits( labels=labels_one_hot, logits=logits)
See our Candidate Sampling Algorithms Reference
Also see Section 3 of Jean et al., 2014 (pdf) for the math.
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, num_true]
. The target classes. Note that this format differs from the labels
argument of nn.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.num_true
: An int
. The number of target classes per training example.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).seed
: random seed for candidate sampling. Default to None, which doesn't set the op-level random seed for candidate sampling.A batch_size
1-D tensor of per-example sampled softmax losses.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/sampled_softmax_loss