tf.feature_column.linear_model

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Returns a linear prediction Tensor based on given feature_columns.

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

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. To be specific, assume the input as SparseTensor looks like:

  shape = [2, 2]
  {
      [0, 0]: "a"
      [1, 0]: "b"
      [1, 1]: "c"
  }

linear_model assigns weights for the presence of "a", "b", "c' implicitly, just like indicator_column, while input_layer explicitly requires wrapping each of categorical columns with an embedding_column or an indicator_column.

Example of usage:

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.io.parse_example(..., features=make_parse_example_spec(columns))
prediction = linear_model(features, columns)

The sparse_combiner argument works as follows For example, for two features represented as the categorical columns:

  # Feature 1

  shape = [2, 2]
  {
      [0, 0]: "a"
      [0, 1]: "b"
      [1, 0]: "c"
  }

  # Feature 2

  shape = [2, 3]
  {
      [0, 0]: "d"
      [1, 0]: "e"
      [1, 1]: "f"
      [1, 2]: "f"
  }

with sparse_combiner as "mean", the linear model outputs consequently are:

  y_0 = 1.0 / 2.0 * ( w_a + w_b ) + w_d + b
  y_1 = w_c + 1.0 / 3.0 * ( w_e + 2.0 * w_f ) + b

where y_i is the output, b is the bias, and w_x is the weight assigned to the presence of x in the input features.