tf.contrib.layers.weighted_sum_from_feature_columns

A tf.contrib.layers style linear prediction builder based on FeatureColumn.

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

Generally a single example in training data is described with feature columns. This function generates weighted sum for each num_outputs. Weighted sum refers to logits in classification problems. It refers to prediction itself for linear regression problems.

Example:

# Building model for training
feature_columns = (
    real_valued_column("my_feature1"),
    ...
)
columns_to_tensor = tf.io.parse_example(...)
logits = weighted_sum_from_feature_columns(
    columns_to_tensors=columns_to_tensor,
    feature_columns=feature_columns,
    num_outputs=1)
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels,
                                               logits=logits)

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 dictionary which maps feature_column to corresponding Variable. A Variable which is used for bias.

Raises
ValueError	if FeatureColumn cannot be used for linear predictions.