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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 softmax 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))
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.
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. |