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Computes and returns the sampled sparse softmax training 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'
)
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.
|