tf.contrib.losses.metric_learning.cluster_loss

Computes the clustering loss.

tf.contrib.losses.metric_learning.cluster_loss(
    labels, embeddings, margin_multiplier, enable_pam_finetuning=True,
    margin_type='nmi', print_losses=False
)

The following structured margins are supported: nmi: normalized mutual information ami: adjusted mutual information ari: adjusted random index vmeasure: v-measure const: indicator checking whether the two clusterings are the same.

Args
`labels`	2-D Tensor of labels of shape [batch size, 1]
`embeddings`	2-D Tensor of embeddings of shape [batch size, embedding dimension]. Embeddings should be l2 normalized.
`margin_multiplier`	float32 scalar. multiplier on the structured margin term See section 3.2 of paper for discussion.
`enable_pam_finetuning`	Boolean, Whether to run local pam refinement. See section 3.4 of paper for discussion.
`margin_type`	Type of structured margin to use. See section 3.2 of paper for discussion. Can be 'nmi', 'ami', 'ari', 'vmeasure', 'const'.
`print_losses`	Boolean. Option to print the loss.

Paper: https://arxiv.org/abs/1612.01213

Returns
`clustering_loss`	A float32 scalar `Tensor`.

Raises
`ImportError`	If sklearn dependency is not installed.

tf.contrib.losses.metric_learning.cluster_loss

Args

Returns

Raises