def load_weights(self, filepath, by_name=False):
if h5py is None:
raise ImportError('`load_weights` requires h5py.')
f = h5py.File(filepath, mode='r')
if 'layer_names' not in f.attrs and 'model_weights' in f:
f = f['model_weights']
layers = self.layers
if by_name:
topology.load_weights_from_hdf5_group_by_name(f, layers)
else:
topology.load_weights_from_hdf5_group(f, layers)
if hasattr(f, 'close'):
f.close()
def load_weights(self, filepath, by_name=False):
if h5py is None:
raise ImportError('`load_weights` requires h5py.')
f = h5py.File(filepath, mode='r')
if 'layer_names' not in f.attrs and 'model_weights' in f:
f = f['model_weights']
layers = self.layers
if by_name:
topology.load_weights_from_hdf5_group_by_name(f, layers)
else:
topology.load_weights_from_hdf5_group(f, layers)
if hasattr(f, 'close'):
f.close()
def load_model(filepath, custom_objects=None):
"""Loads a model saved via `save_model`.
Arguments:
filepath: String, path to the saved model.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
Returns:
A Keras model instance. If an optimizer was found
as part of the saved model, the model is already
compiled. Otherwise, the model is uncompiled and
a warning will be displayed.
Raises:
ImportError: if h5py is not available.
ValueError: In case of an invalid savefile.
"""
if h5py is None:
raise ImportError('`save_model` requires h5py.')
if not custom_objects:
custom_objects = {}
def convert_custom_objects(obj):
"""Handles custom object lookup.
Arguments:
obj: object, dict, or list.
Returns:
The same structure, where occurences
of a custom object name have been replaced
with the custom object.
"""
if isinstance(obj, list):
deserialized = []
for value in obj:
if value in custom_objects:
deserialized.append(custom_objects[value])
else:
deserialized.append(value)
return deserialized
if isinstance(obj, dict):
deserialized = {}
for key, value in obj.items():
if value in custom_objects:
deserialized[key] = custom_objects[value]
else:
deserialized[key] = value
return deserialized
if obj in custom_objects:
return custom_objects[obj]
return obj
f = h5py.File(filepath, mode='r')
# instantiate model
model_config = f.attrs.get('model_config')
if model_config is None:
raise ValueError('No model found in config file.')
model_config = json.loads(model_config.decode('utf-8'))
model = model_from_config(model_config, custom_objects=custom_objects)
# set weights
topology.load_weights_from_hdf5_group(f['model_weights'], model.layers)
# instantiate optimizer
training_config = f.attrs.get('training_config')
if training_config is None:
warnings.warn('No training configuration found in save file: '
'the model was *not* compiled. Compile it manually.')
f.close()
return model
training_config = json.loads(training_config.decode('utf-8'))
optimizer_config = training_config['optimizer_config']
optimizer = optimizers.deserialize(optimizer_config,
custom_objects=custom_objects)
# Recover loss functions and metrics.
loss = convert_custom_objects(training_config['loss'])
metrics = convert_custom_objects(training_config['metrics'])
sample_weight_mode = training_config['sample_weight_mode']
loss_weights = training_config['loss_weights']
# Compile model.
model.compile(optimizer=optimizer,
loss=loss,
metrics=metrics,
loss_weights=loss_weights,
sample_weight_mode=sample_weight_mode)
# Set optimizer weights.
if 'optimizer_weights' in f:
# Build train function (to get weight updates).
if isinstance(model, Sequential):
model.model._make_train_function()
else:
model._make_train_function()
optimizer_weights_group = f['optimizer_weights']
optimizer_weight_names = [
n.decode('utf8')
for n in optimizer_weights_group.attrs['weight_names']
]
optimizer_weight_values = [
optimizer_weights_group[n] for n in optimizer_weight_names
]
model.optimizer.set_weights(optimizer_weight_values)
f.close()
return model
def load_model(filepath, custom_objects=None, compile=True): # pylint: disable=redefined-builtin
"""Loads a model saved via `save_model`.
Arguments:
filepath: String, path to the saved model.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
compile: Boolean, whether to compile the model
after loading.
Returns:
A Keras model instance. If an optimizer was found
as part of the saved model, the model is already
compiled. Otherwise, the model is uncompiled and
a warning will be displayed. When `compile` is set
to False, the compilation is omitted without any
warning.
Raises:
ImportError: if h5py is not available.
ValueError: In case of an invalid savefile.
"""
if h5py is None:
raise ImportError('`load_model` requires h5py.')
if not custom_objects:
custom_objects = {}
def convert_custom_objects(obj):
"""Handles custom object lookup.
Arguments:
obj: object, dict, or list.
Returns:
The same structure, where occurrences
of a custom object name have been replaced
with the custom object.
"""
if isinstance(obj, list):
deserialized = []
for value in obj:
if value in custom_objects:
deserialized.append(custom_objects[value])
else:
deserialized.append(value)
return deserialized
if isinstance(obj, dict):
deserialized = {}
for key, value in obj.items():
deserialized[key] = []
if isinstance(value, list):
for element in value:
if element in custom_objects:
deserialized[key].append(custom_objects[element])
else:
deserialized[key].append(element)
elif value in custom_objects:
deserialized[key] = custom_objects[value]
else:
deserialized[key] = value
return deserialized
if obj in custom_objects:
return custom_objects[obj]
return obj
f = h5py.File(filepath, mode='r')
# instantiate model
model_config = f.attrs.get('model_config')
if model_config is None:
raise ValueError('No model found in config file.')
model_config = json.loads(model_config.decode('utf-8'))
model = model_from_config(model_config, custom_objects=custom_objects)
# set weights
topology.load_weights_from_hdf5_group(f['model_weights'], model.layers)
# Early return if compilation is not required.
if not compile:
f.close()
return model
# instantiate optimizer
training_config = f.attrs.get('training_config')
if training_config is None:
logging.warning('No training configuration found in save file: '
'the model was *not* compiled. Compile it manually.')
f.close()
return model
training_config = json.loads(training_config.decode('utf-8'))
optimizer_config = training_config['optimizer_config']
optimizer = optimizers.deserialize(
optimizer_config, custom_objects=custom_objects)
# Recover loss functions and metrics.
loss = convert_custom_objects(training_config['loss'])
metrics = convert_custom_objects(training_config['metrics'])
sample_weight_mode = training_config['sample_weight_mode']
loss_weights = training_config['loss_weights']
# Compile model.
model.compile(
optimizer=optimizer,
loss=loss,
metrics=metrics,
loss_weights=loss_weights,
sample_weight_mode=sample_weight_mode)
# Set optimizer weights.
if 'optimizer_weights' in f:
# Build train function (to get weight updates).
if isinstance(model, Sequential):
model.model._make_train_function()
else:
model._make_train_function()
optimizer_weights_group = f['optimizer_weights']
optimizer_weight_names = [
n.decode('utf8') for n in optimizer_weights_group.attrs['weight_names']
]
optimizer_weight_values = [
optimizer_weights_group[n] for n in optimizer_weight_names
]
model.optimizer.set_weights(optimizer_weight_values)
f.close()
return model
