def test_serialize_non_feature_column(self):
class NotAFeatureColumn(object):
pass
with self.assertRaisesRegexp(ValueError, 'is not a FeatureColumn'):
serialization.serialize_feature_column(NotAFeatureColumn())
def get_config(self):
from tensorflow.python.feature_column.serialization import serialize_feature_columns, serialize_feature_column
config = {
"item_embedding_size":
self.item_embedding_size,
"item_id_column":
serialize_feature_column(self.item_id_column),
"item_feature_columns":
serialize_feature_columns(self.item_feature_columns),
"target_id_column":
serialize_feature_column(self.target_id_column)
}
base_config = super(EgesModel, self).get_config()
return {**base_config, **config}
def get_config(self):
"""See 'FeatureColumn` base class."""
from tensorflow.python.feature_column.serialization import serialize_feature_column
config = dict(zip(self._fields, self))
config['categorical_column'] = serialize_feature_column(
self.categorical_column)
return config
def test_serialization(self):
"""Tests that column can be serialized."""
def _custom_fn(input_tensor):
return input_tensor + 42
column = sfc.sequence_numeric_column(
key='my-key', shape=(2,), default_value=3, dtype=dtypes.int32,
normalizer_fn=_custom_fn)
configs = serialization.serialize_feature_column(column)
column = serialization.deserialize_feature_column(
configs, custom_objects={_custom_fn.__name__: _custom_fn})
self.assertEqual(column.key, 'my-key')
self.assertEqual(column.shape, (2,))
self.assertEqual(column.default_value, 3)
self.assertEqual(column.normalizer_fn(3), 45)
with self.assertRaisesRegex(ValueError,
'Instance: 0 is not a FeatureColumn'):
serialization.serialize_feature_column(int())
def get_config(self):
from tensorflow.python.feature_column.serialization import (
serialize_feature_column, ) # pylint: disable=g-import-not-at-top
config = dict(zip(self._fields, self))
config["categorical_columns"] = tuple(
[serialize_feature_column(fc) for fc in self.categorical_columns])
return config
def get_config(self):
# Import here to avoid circular imports.
from tensorflow.python.feature_column import serialization # pylint: disable=g-import-not-at-top
column_configs = [serialization.serialize_feature_column(fc)
for fc in self._feature_columns]
config = {'feature_columns': column_configs}
config['partitioner'] = generic_utils.serialize_keras_object(
self._partitioner)
base_config = super( # pylint: disable=bad-super-call
_BaseFeaturesLayer, self).get_config()
return dict(list(base_config.items()) + list(config.items()))