# tf.contrib.layers.weighted_sum_from_feature_columns

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

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.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.