feature_column_ops_test.py 文件源码

def testPredictionsWithWeightedSparseColumn(self):
    language = tf.contrib.layers.sparse_column_with_keys(
        column_name="language",
        keys=["english", "finnish", "hindi"])
    weighted_language = tf.contrib.layers.weighted_sparse_column(
        sparse_id_column=language,
        weight_column_name="age")
    with tf.Graph().as_default():
      features = {
          "language": tf.SparseTensor(values=["hindi", "english"],
                                      indices=[[0, 0], [1, 0]],
                                      shape=[2, 1]),
          "age": tf.SparseTensor(values=[10.0, 20.0],
                                 indices=[[0, 0], [1, 0]],
                                 shape=[2, 1])
      }
      output, column_to_variable, bias = (
          tf.contrib.layers.weighted_sum_from_feature_columns(
              features, [weighted_language], num_outputs=1))
      with self.test_session() as sess:
        tf.initialize_all_variables().run()
        tf.initialize_all_tables().run()

        self.assertAllClose(output.eval(), [[0.], [0.]])

        sess.run(bias.assign([0.1]))
        self.assertAllClose(output.eval(), [[0.1], [0.1]])

        # score: bias + age*language_weight[index]
        sess.run(column_to_variable[weighted_language][0].assign(
            [[0.1], [0.2], [0.3]]))
        self.assertAllClose(output.eval(), [[3.1], [2.1]])