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Creates a Head for multi label classification. (deprecated)

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).

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

An instance of Head for multi label classification.

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