tf.contrib.learn.multi_label_head

Creates a Head for multi label classification. (deprecated)

tf.contrib.learn.multi_label_head(
    n_classes, label_name=None, weight_column_name=None, enable_centered_bias=False,
    head_name=None, thresholds=None, metric_class_ids=None, loss_fn=None
)

Multi-label classification handles the case where each example may have zero or more associated labels, from a discrete set. This is distinct from multi_class_head which has exactly one label from a discrete set.

This head by default uses sigmoid cross entropy loss, which expects as input a multi-hot tensor of shape (batch_size, num_classes).

Args
n_classes	Integer, number of classes, must be >= 2
label_name	String, name of the key in label dict. Can be null if label is a tensor (single headed models).
weight_column_name	A string defining feature column name representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example.
enable_centered_bias	A bool. If True, estimator will learn a centered bias variable for each class. Rest of the model structure learns the residual after centered bias.
head_name	name of the head. If provided, predictions, summary and metrics keys will be suffixed by "/" + head_name and the default variable scope will be head_name.
thresholds	thresholds for eval metrics, defaults to [.5]
metric_class_ids	List of class IDs for which we should report per-class metrics. Must all be in the range [0, n_classes).
loss_fn	Optional function that takes (labels, logits, weights) as parameter and returns a weighted scalar loss. weights should be optional. See tf.losses

Returns
An instance of Head for multi label classification.

Raises
ValueError	If n_classes is < 2
ValueError	If loss_fn does not have expected signature.