feature_column_ops_test.py 文件源码

def testMulticlassWithRealValuedColumnHavingMultiDimensions(self):
    country = tf.contrib.layers.sparse_column_with_hash_bucket(
        "country", hash_bucket_size=5)
    age = tf.contrib.layers.real_valued_column("age")
    # The following RealValuedColumn has 3 dimensions.
    incomes = tf.contrib.layers.real_valued_column("incomes", 3)
    with tf.Graph().as_default():
      features = {"age": tf.constant([[1], [1]]),
                  "incomes": tf.constant([[100., 200., 300.], [10., 20., 30.]]),
                  "country": tf.SparseTensor(values=["US", "SV"],
                                             indices=[[0, 0], [1, 0]],
                                             shape=[2, 2])}
      output, column_to_variable, _ = (
          tf.contrib.layers.weighted_sum_from_feature_columns(
              features, [country, age, incomes],
              num_outputs=5))
      with self.test_session() as sess:
        tf.initialize_all_variables().run()
        tf.initialize_all_tables().run()

        incomes_weights = column_to_variable[incomes][0]
        sess.run(incomes_weights.assign([[0.01, 0.1, 1., 10., 100.],
                                         [0.02, 0.2, 2., 20., 200.],
                                         [0.03, 0.3, 3., 30., 300.]]))
        self.assertAllClose(output.eval(), [[14., 140., 1400., 14000., 140000.],
                                            [1.4, 14., 140., 1400., 14000.]])