def load_h5(cellar_path):
""" return hdf5 model loaded from cellar path """
if not cellar_path[0] == '/':
cellar_path = '/'+cellar+path
config = _configs.Config()
return _load_model(config.resolve_paths(cellar_path))
def __init__(self):
_common_model.__init__(self)
# Define sample directory
self.sample_dir = _os.path.join(self.sample_dir, 'rotation')
# Load compiled model
self._mod = _load_model('rot_mod.h5')
# Load category (i.e. 'class') names
self._class_names = _np.load('rot_class_names.npy')
self.class_name_dict = dict([[v, k] for k, v in self._class_names])
def load_uncompiled_model(filepath):
'''
Loads the specified Keras model without compiling it.
NB Model can be either compiled or uncompiled.
Arguments:
filepath - the filepath of the model
Returns:
model - a keras.models.Sequential object
'''
return _load_model(filepath, compile=False)
def load_model(filepath):
'''
Loads and compiles the specified Keras model.
NB Model must have been pre-compiled with an optimiser
Arguments:
filepath - the filepath of the model
Returns:
model - a keras.models.Sequential object
'''
return _load_model(filepath, custom_objects=CUSTOM_OBJ_DICT)
def load_model(modelf, **kwargs):
from keras.models import clone_model as _clone_model, Sequential, \
load_model as _load_model
# import these since keras doesn't load them by default
#from AdamW import AdamW
#from SGDW import SGDW
custom_objects = kwargs.pop('custom_objects', {})
#custom_objects['AdamW'] = AdamW
#custom_objects['SGDW'] = SGDW
flatten = kwargs.pop('flatten', False)
model = _load_model(modelf, custom_objects=custom_objects, **kwargs)
if flatten and model.layers[0].name.startswith('sequential_'):
_model = _clone_model(model.layers[0])
for layer in model.layers[1:]:
_model.add(layer)
model = _model
return model
def load_model(model):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
return _load_model(model)
def load_model(file_h5):
"""Load model from file."""
model = _load_model(file_h5)
# FIXME see keras#6462
model._make_predict_function()
return model
def load_model() -> Model:
return _load_model(resource_filename(
Requirement.parse("ca2spike"),
"ca2spike/data/model_{}.h5".format(K.backend())),
custom_objects={"_pearson_corr": pearson_corr})
def load_model() -> Model:
return _load_model(str(rotation_model_config.MODEL_PATH))
