tf.contrib.layers.joint_weighted_sum_from_feature_columns

A restricted linear prediction builder based on FeatureColumns.

tf.contrib.layers.joint_weighted_sum_from_feature_columns(
    columns_to_tensors, feature_columns, num_outputs, weight_collections=None,
    trainable=True, scope=None
)

As long as all feature columns are unweighted sparse columns this computes the prediction of a linear model which stores all weights in a single variable.

Args
columns_to_tensors	A mapping from feature column to tensors. 'string' key means a base feature (not-transformed). It can have FeatureColumn as a key too. That means that FeatureColumn is already transformed by input pipeline. For example, inflow may have handled transformations.
feature_columns	A set containing all the feature columns. All items in the set should be instances of classes derived from FeatureColumn.
num_outputs	An integer specifying number of outputs. Default value is 1.
weight_collections	List of graph collections to which weights are added.
trainable	If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
scope	Optional scope for variable_scope.

Returns
A tuple containing: A Tensor which represents predictions of a linear model. A list of Variables storing the weights. A Variable which is used for bias.

Raises
ValueError	if FeatureColumn cannot be used for linear predictions.