def test_from_config(self, units, sparse_combiner, trainable, name):
cols = [
fc.numeric_column('a'),
fc.categorical_column_with_vocabulary_list('b',
vocabulary_list=('1',
'2',
'3')),
fc.categorical_column_with_hash_bucket(key='c', hash_bucket_size=3)
]
orig_layer = fc._LinearModelLayer(cols,
units=units,
sparse_combiner=sparse_combiner,
trainable=trainable,
name=name)
config = orig_layer.get_config()
new_layer = fc._LinearModelLayer.from_config(config)
self.assertEqual(new_layer.name, orig_layer.name)
self.assertEqual(new_layer._units, units)
self.assertEqual(new_layer._sparse_combiner, sparse_combiner)
self.assertEqual(new_layer.trainable, trainable)
self.assertLen(new_layer._feature_columns, 3)
self.assertEqual(new_layer._feature_columns[0].name, 'a')
self.assertEqual(new_layer._feature_columns[1].vocabulary_list,
('1', '2', '3'))
self.assertEqual(new_layer._feature_columns[2].num_buckets, 3)
def test_get_config(self, units, sparse_combiner, trainable, name):
cols = [fc.numeric_column('a'),
fc.categorical_column_with_identity(key='b', num_buckets=3)]
layer = fc._LinearModelLayer(
cols, units=units, sparse_combiner=sparse_combiner,
trainable=trainable, name=name)
config = layer.get_config()
self.assertEqual(config['name'], layer.name)
self.assertEqual(config['trainable'], trainable)
self.assertEqual(config['units'], units)
self.assertEqual(config['sparse_combiner'], sparse_combiner)
self.assertLen(config['feature_columns'], 2)
self.assertEqual(
config['feature_columns'][0]['class_name'], 'NumericColumn')
self.assertEqual(
config['feature_columns'][1]['class_name'], 'IdentityCategoricalColumn')