tf.contrib.estimator.logistic_regression_head

tf.contrib.estimator.logistic_regression_head(
    weight_column=None,
    loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE,
    name=None
)

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

Creates a _Head for logistic regression.

Uses sigmoid_cross_entropy_with_logits loss, which is the same as binary_classification_head. The differences compared to binary_classification_head are:

  • Does not support label_vocabulary. Instead, labels must be float in the range [0, 1].
  • Does not calculate some metrics that do not make sense, such as AUC.
  • In PREDICT mode, only returns logits and predictions (=tf.sigmoid(logits)), whereas binary_classification_head also returns probabilities, classes, and class_ids.
  • Export output defaults to RegressionOutput, whereas binary_classification_head defaults to PredictOutput.

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

The labels shape must match logits, namely [D0, D1, ... DN] or [D0, D1, ... DN, 1].

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

This is implemented as a generalized linear model, see https://en.wikipedia.org/wiki/Generalized_linear_model.

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

my_head = tf.contrib.estimator.logistic_regression_head()
my_estimator = tf.contrib.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.logistic_regression_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)

Args:

  • 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.
  • loss_reduction: One of tf.losses.Reduction except NONE. Describes how to reduce training loss over batch and label dimension. Defaults to SUM_OVER_BATCH_SIZE, namely weighted sum of losses divided by batch size * label_dimension. See tf.losses.Reduction.
  • 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 logistic regression.

Raises:

  • ValueError: If loss_reduction is invalid.