# tf.feature_column.linear_model

tf.feature_column.linear_model(
features,
feature_columns,
units=1,
sparse_combiner='sum',
weight_collections=None,
trainable=True,
cols_to_vars=None
)


Returns a linear prediction Tensor based on given feature_columns.

This function generates a weighted sum based on output dimension units. Weighted sum refers to logits in classification problems. It refers to the prediction itself for linear regression problems.

Note on supported columns: linear_model treats categorical columns as indicator_columns while input_layer explicitly requires wrapping each of them with an embedding_column or an indicator_column.

Example:

price = numeric_column('price')
price_buckets = bucketized_column(price, boundaries=[0., 10., 100., 1000.])
keywords = categorical_column_with_hash_bucket("keywords", 10K)
keywords_price = crossed_column('keywords', price_buckets, ...)
columns = [price_buckets, keywords, keywords_price ...]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
prediction = linear_model(features, columns)


#### Args:

• features: A mapping from key to tensors. _FeatureColumns look up via these keys. For example numeric_column('price') will look at 'price' key in this dict. Values are Tensor or SparseTensor depending on corresponding _FeatureColumn.
• feature_columns: An iterable containing the FeatureColumns to use as inputs to your model. All items should be instances of classes derived from _FeatureColumns.
• units: An integer, dimensionality of the output space. Default value is 1.
• sparse_combiner: A string specifying how to reduce if a sparse column is multivalent. Currently "mean", "sqrtn" and "sum" are supported, with "sum" the default. "sqrtn" often achieves good accuracy, in particular with bag-of-words columns. It combines each sparse columns independently.
• "sum": do not normalize features in the column
• "mean": do l1 normalization on features in the column
• "sqrtn": do l2 normalization on features in the column
• weight_collections: A list of collection names to which the Variable will be added. Note that, variables will also be added to collections tf.GraphKeys.GLOBAL_VARIABLES and ops.GraphKeys.MODEL_VARIABLES.
• trainable: If True also add the variable to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
• cols_to_vars: If not None, must be a dictionary that will be filled with a mapping from _FeatureColumn to associated list of Variables. For example, after the call, we might have cols_to_vars = { _NumericColumn( key='numeric_feature1', shape=(1,): [], 'bias': [], _NumericColumn( key='numeric_feature2', shape=(2,)): []} If a column creates no variables, its value will be an empty list. Note that cols_to_vars will also contain a string key 'bias' that maps to a list of Variables.

#### Returns:

A Tensor which represents predictions/logits of a linear model. Its shape is (batch_size, units) and its dtype is float32.

#### Raises:

• ValueError: if an item in feature_columns is neither a _DenseColumn nor _CategoricalColumn.