tf.contrib.estimator.multi_label_head

tf.contrib.estimator.multi_label_head(
    n_classes,
    weight_column=None,
    thresholds=None,
    label_vocabulary=None,
    loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE,
    loss_fn=None,
    name=None
)

Defined in tensorflow/contrib/estimator/python/estimator/head.py.

Creates a _Head for multi-label classification.

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 per example.

Uses sigmoid_cross_entropy loss average over classes and weighted sum over the batch. Namely, if the input logits have shape [batch_size, n_classes], the loss is the average over n_classes and the weighted sum over batch_size.

The head expects logits with shape [D0, D1, ... DN, n_classes]. In many applications, the shape is [batch_size, n_classes].

Labels can be: * A multi-hot tensor of shape [D0, D1, ... DN, n_classes] * An integer SparseTensor of class indices. The dense_shape must be [D0, D1, ... DN, ?] and the values within [0, n_classes). * If label_vocabulary is given, a string SparseTensor. The dense_shape must be [D0, D1, ... DN, ?] and the values within label_vocabulary.

If weight_column is specified, weights must be of shape [D0, D1, ... DN], or [D0, D1, ... DN, 1].

Also supports custom loss_fn. loss_fn takes (labels, logits) or (labels, logits, features) as arguments and returns unreduced loss with shape [D0, D1, ... DN, 1]. loss_fn must support indicator labels with shape [D0, D1, ... DN, n_classes]. Namely, the head applies label_vocabulary to the input labels before passing them to loss_fn.

Args:

  • n_classes: Number of classes, must be greater than 1 (for 1 class, use binary_classification_head).
  • weight_column: A string or a _NumericColumn created by tf.feature_column.numeric_column defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. Per-class weighting is not supported.
  • thresholds: Iterable of floats in the range (0, 1). Accuracy, precision and recall metrics are evaluated for each threshold value. The threshold is applied to the predicted probabilities, i.e. above the threshold is true, below is false.
  • label_vocabulary: A list of strings represents possible label values. If it is not given, that means labels are already encoded as integer within [0, n_classes) or multi-hot Tensor. If given, labels must be SparseTensor string type and have any value in label_vocabulary. Also there will be errors if vocabulary is not provided and labels are string.
  • loss_reduction: One of tf.losses.Reduction except NONE. Describes how to reduce training loss over batch. Defaults to SUM_OVER_BATCH_SIZE, namely weighted sum of losses divided by batch size. See tf.losses.Reduction.
  • loss_fn: Optional loss function.
  • name: name of the head. If provided, summary and metrics keys will be suffixed by "/" + name. Also used as name_scope when creating ops.

Returns:

An instance of _Head for multi-label classification.

Raises:

  • ValueError: if n_classes, thresholds, loss_reduction or loss_fn is invalid.