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# 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'
)
```

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)
```

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.

© 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/contrib/nn/sampled_sparse_softmax_loss