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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.
# 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)
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,
inflowmay 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.
Truealso add variables to the graph collection
scope: Optional scope for variable_scope.
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.
ValueError: if FeatureColumn cannot be used for linear predictions.