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
.
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
: IfTrue
also add variables to the graph collectionGraphKeys.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.