tf.contrib.layers.joint_weighted_sum_from_feature_columns

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

Defined in tensorflow/contrib/layers/python/layers/feature_column_ops.py.

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.