def _python_properties_internal(self):
metadata = super(ModelSavedModelSaver, self)._python_properties_internal()
# Network stateful property is dependent on the child layers.
metadata.pop('stateful')
metadata['is_graph_network'] = self.obj._is_graph_network # pylint: disable=protected-access
metadata['save_spec'] = self.obj._get_save_spec(dynamic_batch=False) # pylint: disable=protected-access
metadata.update(
saving_utils.model_metadata(
self.obj, include_optimizer=True, require_config=False))
return metadata
def _python_properties_internal(self):
metadata = super()._python_properties_internal()
# Network stateful property is dependent on the child layers.
metadata.pop('stateful')
metadata['is_graph_network'] = self.obj._is_graph_network # pylint: disable=protected-access
spec = self.obj.save_spec(dynamic_batch=False)
metadata['full_save_spec'] = spec
# save_spec is saved for forward compatibility on older TF versions.
metadata['save_spec'] = None if spec is None else spec[0][0]
metadata.update(
saving_utils.model_metadata(self.obj,
include_optimizer=True,
require_config=False))
return metadata
def save_model_to_hdf5(model, filepath, overwrite=True, include_optimizer=True):
"""Saves a model to a HDF5 file.
The saved model contains:
- the model's configuration (topology)
- the model's weights
- the model's optimizer's state (if any)
Thus the saved model can be reinstantiated in
the exact same state, without any of the code
used for model definition or training.
Args:
model: Keras model instance to be saved.
filepath: One of the following:
- String, path where to save the model
- `h5py.File` object where to save the model
overwrite: Whether we should overwrite any existing
model at the target location, or instead
ask the user with a manual prompt.
include_optimizer: If True, save optimizer's state together.
Raises:
ImportError: if h5py is not available.
"""
if h5py is None:
raise ImportError('`save_model()` using h5 format requires h5py. Could not '
'import h5py.')
# TODO(psv) Add warning when we save models that contain non-serializable
# entities like metrics added using `add_metric` and losses added using
# `add_loss.`
if len(model.weights) != len(model._undeduplicated_weights):
logging.warning('Found duplicated `Variable`s in Model\'s `weights`. '
'This is usually caused by `Variable`s being shared by '
'Layers in the Model. These `Variable`s will be treated '
'as separate `Variable`s when the Model is restored. To '
'avoid this, please save with `save_format="tf"`.')
if not isinstance(filepath, h5py.File):
# If file exists and should not be overwritten.
if not overwrite and os.path.isfile(filepath):
proceed = ask_to_proceed_with_overwrite(filepath)
if not proceed:
return
# Try creating dir if not exist
dirpath = os.path.dirname(filepath)
if not os.path.exists(dirpath):
tf.io.gfile.makedirs(dirpath)
f = h5py.File(filepath, mode='w')
opened_new_file = True
else:
f = filepath
opened_new_file = False
try:
model_metadata = saving_utils.model_metadata(model, include_optimizer)
for k, v in model_metadata.items():
if isinstance(v, (dict, list, tuple)):
f.attrs[k] = json.dumps(
v, default=json_utils.get_json_type).encode('utf8')
else:
f.attrs[k] = v
model_weights_group = f.create_group('model_weights')
save_weights_to_hdf5_group(model_weights_group, model)
# TODO(b/128683857): Add integration tests between tf.keras and external
# Keras, to avoid breaking TF.js users.
if (include_optimizer and model.optimizer and
not isinstance(model.optimizer, optimizer_v1.TFOptimizer)):
save_optimizer_weights_to_hdf5_group(f, model.optimizer)
f.flush()
finally:
if opened_new_file:
f.close()