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


Creates a Head for multi label classification. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please switch to tf.contrib.estimator.*_head.

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.