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.
|