tf.feature_column.linear_model( features, feature_columns, units=1, sparse_combiner='sum', weight_collections=None, trainable=True, cols_to_vars=None )
Defined in tensorflow/python/feature_column/feature_column.py
.
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_column
s 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)
features
: A mapping from key to tensors. _FeatureColumn
s 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 _FeatureColumn
s.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.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 Variable
s. For example, after the call, we might have cols_to_vars = { _NumericColumn( key='numeric_feature1', shape=(1,): [A Tensor
which represents predictions/logits of a linear model. Its shape is (batch_size, units) and its dtype is float32
.
ValueError
: if an item in feature_columns
is neither a _DenseColumn
nor _CategoricalColumn
.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/feature_column/linear_model