dnn_linear_combined_test.py 文件源码

def testLinearOnlyOneFeature(self):
    """Tests that linear-only instantiation works for one feature only."""
    def input_fn():
      return {
          'language': tf.SparseTensor(values=['english'],
                                      indices=[[0, 0]],
                                      shape=[1, 1])
      }, tf.constant([[1]])

    language = tf.contrib.layers.sparse_column_with_hash_bucket('language', 99)

    classifier = tf.contrib.learn.DNNLinearCombinedClassifier(
        linear_feature_columns=[language])
    classifier.fit(input_fn=input_fn, steps=100)
    loss1 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
    classifier.fit(input_fn=input_fn, steps=200)
    loss2 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
    self.assertLess(loss2, loss1)
    self.assertLess(loss2, 0.01)
    self.assertTrue('centered_bias_weight' in classifier.get_variable_names())

    self.assertNotIn('dnn/logits/biases', classifier.get_variable_names())
    self.assertNotIn('dnn/logits/weights', classifier.get_variable_names())
    self.assertEquals(1, len(classifier.linear_bias_))
    self.assertEquals(99, len(classifier.linear_weights_))