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

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

See the guide: Layers (contrib) > Feature columns

A restricted linear prediction builder based on FeatureColumns.

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.