tf.contrib.distributions.estimator_head_distribution_regression

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Creates a Head for regression under a generic distribution. (deprecated)

tf.contrib.distributions.estimator_head_distribution_regression(
    make_distribution_fn, label_dimension=1, logits_dimension=None, label_name=None,
    weight_column_name=None, enable_centered_bias=False, head_name=None
)

Args
make_distribution_fn	Python callable which returns a tf.Distribution instance created using only logits.
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]).
logits_dimension	Number of logits per example. This is the size of the last dimension of the logits Tensor (typically, this has shape [batch_size, logits_dimension]). Default value: label_dimension.
label_name	Python str, name of the key in label dict. Can be None if label is a Tensor (single headed models).
weight_column_name	Python str defining feature column name representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example.
enable_centered_bias	Python bool. If True, estimator will learn a centered bias variable for each class. Rest of the model structure learns the residual after centered bias.
head_name	Python str, name of the head. Predictions, summary and metrics keys are suffixed by "/" + head_name and the default variable scope is head_name.

Returns
An instance of Head for generic regression.