def _revive_layer_or_model_from_config(self, metadata, node_id):
"""Revives a layer/custom model from config; returns None if infeasible."""
# Check that the following requirements are met for reviving from config:
# 1. Object can be deserialized from config.
# 2. If the object needs to be built, then the build input shape can be
# found.
class_name = metadata.get('class_name')
config = metadata.get('config')
shared_object_id = metadata.get('shared_object_id')
must_restore_from_config = metadata.get('must_restore_from_config')
if not generic_utils.validate_config(config):
return None
try:
obj = layers_module.deserialize(
generic_utils.serialize_keras_class_and_config(
class_name, config, shared_object_id=shared_object_id))
except ValueError:
if must_restore_from_config:
raise RuntimeError(
'Unable to restore a layer of class {cls}. Layers of '
'class {cls} require that the class be provided to '
'the model loading code, either by registering the '
'class using @keras.utils.register_keras_serializable '
'on the class def and including that file in your '
'program, or by passing the class in a '
'keras.utils.CustomObjectScope that wraps this load '
'call.'.format(cls=class_name))
else:
return None
# Use the dtype, name, and trainable status. Often times these are not
# specified in custom configs, so retrieve their values from the metadata.
# pylint: disable=protected-access
obj._name = metadata['name']
if metadata.get('trainable') is not None:
obj.trainable = metadata['trainable']
if metadata.get('dtype') is not None:
obj._set_dtype_policy(metadata['dtype'])
if metadata.get('stateful') is not None:
obj.stateful = metadata['stateful']
# Restore model save spec for subclassed models. (layers do not store a
# SaveSpec)
if isinstance(obj, training_lib.Model):
save_spec = metadata.get('save_spec')
if save_spec is not None:
obj._set_save_spec(save_spec)
# pylint: enable=protected-access
build_input_shape = metadata.get('build_input_shape')
built = self._try_build_layer(obj, node_id, build_input_shape)
if not built:
# If the layer cannot be built, revive a custom layer instead.
return None
return obj
def serialize_feature_column(fc):
"""Serializes a FeatureColumn or a raw string key.
This method should only be used to serialize parent FeatureColumns when
implementing FeatureColumn._get_config(), else serialize_feature_columns()
is preferable.
This serialization also keeps information of the FeatureColumn class, so
deserialization is possible without knowing the class type. For example:
a = numeric_column('x')
a._get_config() gives:
{
'key': 'price',
'shape': (1,),
'default_value': None,
'dtype': 'float32',
'normalizer_fn': None
}
While serialize_feature_column(a) gives:
{
'class_name': 'NumericColumn',
'config': {
'key': 'price',
'shape': (1,),
'default_value': None,
'dtype': 'float32',
'normalizer_fn': None
}
}
Args:
fc: A FeatureColumn or raw feature key string.
Returns:
Keras serialization for FeatureColumns, leaves string keys unaffected.
Raises:
ValueError if called with input that is not string or FeatureColumn.
"""
# Import here to avoid circular imports.
from tensorflow.python.keras.utils import generic_utils # pylint: disable=g-import-not-at-top
if isinstance(fc, six.string_types):
return fc
elif isinstance(fc, fc_lib.FeatureColumn):
return generic_utils.serialize_keras_class_and_config(
fc.__class__.__name__, fc._get_config()) # pylint: disable=protected-access
else:
raise ValueError('Instance: {} is not a FeatureColumn'.format(fc))
def _revive_metric_from_config(self, metadata, node_id):
class_name = compat.as_str(metadata['class_name'])
config = metadata.get('config')
if not generic_utils.validate_config(config):
return None
try:
obj = metrics.deserialize(
generic_utils.serialize_keras_class_and_config(class_name, config))
except ValueError:
return None
build_input_shape = metadata.get('build_input_shape')
if build_input_shape is not None and hasattr(obj, '_build'):
obj._build(build_input_shape) # pylint: disable=protected-access
return obj
def _revive_layer_from_config(self, metadata, node_id):
"""Revives a layer from config, or returns None if infeasible."""
# Check that the following requirements are met for reviving from config:
# 1. Object can be deserialized from config.
# 2. If the object needs to be built, then the build input shape can be
# found.
class_name = metadata.get('class_name')
config = metadata.get('config')
if not generic_utils.validate_config(config):
return None
try:
obj = layers_module.deserialize(
generic_utils.serialize_keras_class_and_config(
class_name, config))
except ValueError:
return None
# Use the dtype, name, and trainable status. Often times these are not
# specified in custom configs, so retrieve their values from the metadata.
# pylint: disable=protected-access
obj._name = metadata['name']
if metadata.get('trainable') is not None:
obj.trainable = metadata['trainable']
if metadata.get('dtype') is not None:
obj._set_dtype_policy(metadata['dtype'])
if metadata.get('stateful') is not None:
obj.stateful = metadata['stateful']
# pylint: enable=protected-access
build_input_shape = metadata.get('build_input_shape')
built = self._try_build_layer(obj, node_id, build_input_shape)
if not built:
# If the layer cannot be built, revive a custom layer instead.
return None
return obj
def _revive_layer_from_config(self, metadata, node_id):
"""Revives a layer from config, or returns None if infeasible."""
# Check that the following requirements are met for reviving from config:
# 1. Object can be deserialized from config.
# 2. If the object needs to be built, then the build input shape can be
# found.
class_name = metadata.get('class_name')
config = metadata.get('config')
if config is None or generic_utils.LAYER_UNDEFINED_CONFIG_KEY in config:
return None
try:
obj = layers_module.deserialize(
generic_utils.serialize_keras_class_and_config(
class_name, config))
except ValueError:
return None
# Use the dtype, name, and trainable status. Often times these are not
# specified in custom configs, so retrieve their values from the metadata.
# pylint: disable=protected-access
obj._name = metadata['name']
if metadata.get('trainable') is not None:
obj.trainable = metadata['trainable']
if metadata.get('dtype') is not None:
obj._set_dtype_policy(metadata['dtype'])
# pylint: enable=protected-access
input_shape = None
if not isinstance(obj, input_layer.InputLayer):
input_shape = self._infer_inputs(node_id, convert_to_shapes=True)
if input_shape is None:
return None
obj.build(input_shape)
obj.built = True
return obj
