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TensorFlow 1 version
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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 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))
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 | |
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weights
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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.
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biases
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A Tensor of shape [num_classes]. The class biases.
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labels
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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.
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inputs
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A Tensor of shape [batch_size, dim]. The forward activations of
the input network.
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num_sampled
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An int. The number of classes to randomly sample per batch.
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num_classes
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An int. The number of possible classes.
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num_true
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An int. The number of target classes per training example.
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sampled_values
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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)
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remove_accidental_hits
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A bool. whether to remove "accidental hits"
where a sampled class equals one of the target classes. Default is True.
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seed
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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 | |
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A batch_size 1-D tensor of per-example sampled softmax losses.
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