tf.contrib.estimator.regression_head

tf.contrib.estimator.regression_head(
    weight_column=None,
    label_dimension=1,
    loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE,
    loss_fn=None,
    inverse_link_fn=None,
    name=None
)

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

Creates a _Head for regression using the mean_squared_error loss.

The loss is the weighted sum over all input dimensions. Namely, if the input labels have shape [batch_size, label_dimension], the loss is the weighted sum over both batch_size and label_dimension.

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

The labels shape must match logits, namely [D0, D1, ... DN, label_dimension]. If label_dimension=1, shape [D0, D1, ... DN] is also supported.

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

Supports custom loss_fn. loss_fn takes (labels, logits) or (labels, logits, features) as arguments and returns unreduced loss with shape [D0, D1, ... DN, label_dimension].

Also supports custom inverse_link_fn, also known as 'mean function'. inverse_link_fn is only used in PREDICT mode. It takes logits as argument and returns predicted values. This function is the inverse of the link function defined in https://en.wikipedia.org/wiki/Generalized_linear_model#Link_function Namely, for poisson regression, set inverse_link_fn=tf.exp.

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

my_head = tf.contrib.estimator.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.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.
  • label_dimension: Number of regression labels per example. This is the size of the last dimension of the labels Tensor (typically, this has shape [batch_size, label_dimension]).
  • 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.
  • loss_fn: Optional loss function. Defaults to mean_squared_error.
  • inverse_link_fn: Optional inverse link function, also known as 'mean function'. Defaults to identity.
  • 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 linear regression.

Raises:

  • ValueError: If label_dimension or loss_reduction is invalid.