tf.contrib.learn.LinearClassifier

education = sparse_column_with_hash_bucket(column_name="education",
                                           hash_bucket_size=1000)
occupation = sparse_column_with_hash_bucket(column_name="occupation",
                                            hash_bucket_size=1000)

education_x_occupation = crossed_column(columns=[education, occupation],
                                        hash_bucket_size=10000)

# Estimator using the default optimizer.
estimator = LinearClassifier(
    feature_columns=[occupation, education_x_occupation])

# Or estimator using the FTRL optimizer with regularization.
estimator = LinearClassifier(
    feature_columns=[occupation, education_x_occupation],
    optimizer=tf.train.FtrlOptimizer(
      learning_rate=0.1,
      l1_regularization_strength=0.001
    ))

# Or estimator using the SDCAOptimizer.
estimator = LinearClassifier(
   feature_columns=[occupation, education_x_occupation],
   optimizer=tf.contrib.linear_optimizer.SDCAOptimizer(
     example_id_column='example_id',
     num_loss_partitions=...,
     symmetric_l2_regularization=2.0
   ))

# Input builders
def input_fn_train: # returns x, y
  ...
def input_fn_eval: # returns x, y
  ...
estimator.fit(input_fn=input_fn_train)
estimator.evaluate(input_fn=input_fn_eval)
estimator.predict(x=x)