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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
: 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 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.