tf.contrib.estimator.binary_classification_head

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Creates a _Head for single label binary classification.

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

This head uses sigmoid_cross_entropy_with_logits loss.

The head expects logits with shape [D0, D1, ... DN, 1]. In many applications, the shape is [batch_size, 1].

labels must be a dense Tensor with shape matching logits, namely [D0, D1, ... DN, 1]. If label_vocabulary given, labels must be a string Tensor with values from the vocabulary. If label_vocabulary is not given, labels must be float Tensor with values in the interval [0, 1].

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

The loss is the weighted sum over the input dimensions. Namely, if the input labels have shape [batch_size, 1], the loss is the weighted sum over batch_size.

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 float labels with shape [D0, D1, ... DN, 1]. 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.contrib.estimator.binary_classification_head()
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.contrib.estimator.binary_classification_head()
  logits = tf.keras.Model(...)(features)

  return my_head.create_estimator_spec(
      features=features,
      mode=mode,
      labels=labels,
      optimizer=tf.AdagradOptimizer(learning_rate=0.1),
      logits=logits)

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

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.
thresholds Iterable of floats in the range (0, 1). For binary classification metrics such as precision and recall, an eval metric is generated for each threshold value. This threshold is applied to the logistic values to determine the binary classification (i.e., above the threshold is true, below is false.
label_vocabulary A list or tuple of strings representing possible label values. If it is not given, labels must be float with values within [0, 1]. If given, labels must be string type and have any value in label_vocabulary. Note that errors will be raised if label_vocabulary is not provided but labels are strings.
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.

An instance of _Head for binary classification.

ValueError If thresholds contains a value outside of (0, 1).
ValueError If loss_reduction is invalid.