feature_column_ops_test.py 文件源码

def testJointPredictions(self):
    country = tf.contrib.layers.sparse_column_with_keys(
        column_name="country",
        keys=["us", "finland"])
    language = tf.contrib.layers.sparse_column_with_keys(
        column_name="language",
        keys=["english", "finnish", "hindi"])
    with tf.Graph().as_default():
      features = {
          "country": tf.SparseTensor(values=["finland", "us"],
                                     indices=[[0, 0], [1, 0]],
                                     shape=[2, 1]),
          "language": tf.SparseTensor(values=["hindi", "english"],
                                      indices=[[0, 0], [1, 0]],
                                      shape=[2, 1]),
      }
      output, variables, bias = (
          tf.contrib.layers.joint_weighted_sum_from_feature_columns(
              features, [country, language], num_outputs=1))
      # Assert that only a single weight is created.
      self.assertEqual(len(variables), 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]])

        # shape is [5,1] because 1 class and 2 + 3 features.
        self.assertEquals(variables[0].get_shape().as_list(), [5, 1])

        # score: bias + country_weight + language_weight
        sess.run(variables[0].assign([[0.1], [0.2], [0.3], [0.4], [0.5]]))
        self.assertAllClose(output.eval(), [[0.8], [0.5]])