tf.contrib.estimator.regression_head

Creates a _Head for regression using the mean_squared_error loss.

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
)

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.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.

tf.contrib.estimator.regression_head

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