tf.estimator.MultiLabelHead

View source on GitHub

Class MultiLabelHead

Creates a Head for multi-label classification.

Inherits From: Head

Aliases:

Multi-label classification handles the case where each example may have zero or more associated labels, from a discrete set. This is distinct from MultiClassHead 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 or a multi-hot tensor of shape [D0, D1, ... DN, n_classes].

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.

The head can be used with a canned estimator. Example:

my_head = tf.estimator.MultiLabelHead(n_classes=3)
my_estimator = tf.estimator.DNNEstimator(
    head=my_head,
    hidden_units=...,
    feature_columns=...)

It can also be used with a custom model_fn. Example:

def _my_model_fn(features, labels, mode):
  my_head = tf.estimator.MultiLabelHead(n_classes=3)
  logits = tf.keras.Model(...)(features)

  return my_head.create_estimator_spec(
      features=features,
      mode=mode,
      labels=labels,
      optimizer=tf.keras.optimizers.Adagrad(lr=0.1),
      logits=logits)

my_estimator = tf.estimator.Estimator(model_fn=_my_model_fn)

Args:

  • n_classes: Number of classes, must be greater than 1 (for 1 class, use BinaryClassHead).
  • 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. Decides how to reduce training loss over batch. Defaults to SUM_OVER_BATCH_SIZE, namely weighted sum of losses divided by batch size.
  • loss_fn: Optional loss function.
  • classes_for_class_based_metrics: List of integer class IDs or string class names for which per-class metrics are evaluated. If integers, all must be in the range [0, n_classes - 1]. If strings, all must be in label_vocabulary.
  • name: Name of the head. If provided, summary and metrics keys will be suffixed by "/" + name. Also used as name_scope when creating ops.

__init__

View source

__init__(
    n_classes,
    weight_column=None,
    thresholds=None,
    label_vocabulary=None,
    loss_reduction=losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
    loss_fn=None,
    classes_for_class_based_metrics=None,
    name=None
)

Initialize self. See help(type(self)) for accurate signature.

Properties

logits_dimension

See base_head.Head for details.

loss_reduction

See base_head.Head for details.

name

See base_head.Head for details.

Methods

tf.estimator.MultiLabelHead.create_estimator_spec

View source

create_estimator_spec(
    features,
    mode,
    logits,
    labels=None,
    optimizer=None,
    trainable_variables=None,
    train_op_fn=None,
    update_ops=None,
    regularization_losses=None
)

Returns EstimatorSpec that a model_fn can return.

It is recommended to pass all args via name.

Args:

  • features: Input dict mapping string feature names to Tensor or SparseTensor objects containing the values for that feature in a minibatch. Often to be used to fetch example-weight tensor.
  • mode: Estimator's ModeKeys.
  • logits: Logits Tensor to be used by the head.
  • labels: Labels Tensor, or dict mapping string label names to Tensor objects of the label values.
  • optimizer: An tf.keras.optimizers.Optimizer instance to optimize the loss in TRAIN mode. Namely, sets train_op = optimizer.get_updates(loss, trainable_variables), which updates variables to minimize loss.
  • trainable_variables: A list or tuple of Variable objects to update to minimize loss. In Tensorflow 1.x, by default these are the list of variables collected in the graph under the key GraphKeys.TRAINABLE_VARIABLES. As Tensorflow 2.x doesn't have collections and GraphKeys, trainable_variables need to be passed explicitly here.
  • train_op_fn: Function that takes a scalar loss Tensor and returns an op to optimize the model with the loss in TRAIN mode. Used if optimizer is None. Exactly one of train_op_fn and optimizer must be set in TRAIN mode. By default, it is None in other modes. If you want to optimize loss yourself, you can pass lambda _: tf.no_op() and then use EstimatorSpec.loss to compute and apply gradients.
  • update_ops: A list or tuple of update ops to be run at training time. For example, layers such as BatchNormalization create mean and variance update ops that need to be run at training time. In Tensorflow 1.x, these are thrown into an UPDATE_OPS collection. As Tensorflow 2.x doesn't have collections, update_ops need to be passed explicitly here.
  • regularization_losses: A list of additional scalar losses to be added to the training loss, such as regularization losses.

Returns:

EstimatorSpec.

tf.estimator.MultiLabelHead.loss

View source

loss(
    labels,
    logits,
    features=None,
    mode=None,
    regularization_losses=None
)

Returns regularized training loss. See base_head.Head for details.

tf.estimator.MultiLabelHead.metrics

View source

metrics(regularization_losses=None)

Creates metrics. See base_head.Head for details.

tf.estimator.MultiLabelHead.predictions

View source

predictions(
    logits,
    keys=None
)

Return predictions based on keys. See base_head.Head for details.

Args:

  • logits: logits Tensor with shape [D0, D1, ... DN, logits_dimension]. For many applications, the shape is [batch_size, logits_dimension].
  • keys: a list of prediction keys. Key can be either the class variable of prediction_keys.PredictionKeys or its string value, such as: prediction_keys.PredictionKeys.LOGITS or 'logits'.

Returns:

A dict of predictions.

tf.estimator.MultiLabelHead.update_metrics

View source

update_metrics(
    eval_metrics,
    features,
    logits,
    labels,
    regularization_losses=None
)

Updates eval metrics. See base_head.Head for details.