# tf.nn.sampled_softmax_loss

Computes and returns the sampled softmax training loss.

``````tf.nn.sampled_softmax_loss(
weights, biases, labels, inputs, num_sampled, num_classes, num_true=1,
sampled_values=None, remove_accidental_hits=True, seed=None,
name='sampled_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 sigmoid loss for evaluation or inference as in the following example:

``````if mode == "train":
loss = tf.nn.sampled_softmax_loss(
weights=weights,
biases=biases,
labels=labels,
inputs=inputs,
...)
elif mode == "eval":
logits = tf.matmul(inputs, tf.transpose(weights))
labels_one_hot = tf.one_hot(labels, n_classes)
loss = tf.nn.softmax_cross_entropy_with_logits(
labels=labels_one_hot,
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, 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.
• `seed`: random seed for candidate sampling. Default to None, which doesn't set the op-level random seed for candidate sampling.
• `name`: A name for the operation (optional).

#### Returns:

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