def build_model(self, local_session=True):
if type(self.filename) == list:
models = []
for fn in self.filename:
models.append(load_model(filename=fn))
return MPIModel(models=models)
else:
return MPIModel(
model=load_model(filename=self.filename,
custom_objects=self.custom_objects,
weights_file=self.weights))
def build_model(self):
if type(self.filename) == list:
models = []
for fn in self.filename:
models.append(load_model(filename=fn))
return MPIModel(models=models)
else:
return MPIModel(
model=load_model(filename=self.filename,
json_str=self.json_str,
custom_objects=self.custom_objects,
weights_file=self.weights))
def build_model_aux(self):
import keras.backend as K
with K.tf.device(self.device):
if type(self.filename) == list:
models = []
self.weights = self.weights.split(
',') if self.weights else [None] * len(self.filename)
for fn, w in zip(self.filename, self.weights):
models.append(load_model(filename=fn, weights_file=w))
return MPIModel(models=models)
else:
model = load_model(filename=self.filename,
model=self.model,
custom_objects=self.custom_objects,
weights_file=self.weights)
return MPIModel(model=model)
def build_model(self):
import keras.backend as K
K.set_session( K.tf.Session( config=K.tf.ConfigProto(
allow_soft_placement=True, log_device_placement=False,
gpu_options=K.tf.GPUOptions(
per_process_gpu_memory_fraction=1./self.comm.Get_size()) ) ) )
with K.tf.device(self.device):
model = load_model(filename=self.filename, json_str=self.json_str,
custom_objects=self.custom_objects, weights_file=self.weights)
return model
def build_model(self):
import keras.backend as K
K.set_session(
K.tf.Session(config=K.tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
gpu_options=K.tf.GPUOptions(
per_process_gpu_memory_fraction=1. /
self.comm.Get_size()))))
with K.tf.device(self.device):
if type(self.filename) == list:
models = []
self.weights = self.weights.split(
',') if self.weights else [None] * len(self.filename)
for fn, w in zip(self.filename, self.weights):
models.append(load_model(filename=fn, weights_file=w))
return MPIModel(models=models)
else:
model = load_model(filename=self.filename,
json_str=self.json_str,
custom_objects=self.custom_objects,
weights_file=self.weights)
return MPIModel(model=model)
def build_model(self):
import keras.backend as K
K.set_session(
K.tf.Session(config=K.tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
gpu_options=K.tf.GPUOptions(
per_process_gpu_memory_fraction=1. /
self.comm.Get_size()))))
with K.tf.device(self.device):
model = load_model(filename=self.filename,
json_str=self.json_str,
custom_objects=self.custom_objects,
weights_file=self.weights)
return model
def build_model(self):
return load_model(filename=self.filename,
json_str=self.json_str,
custom_objects=self.custom_objects,
weights_file=self.weights)
def build_model(self):
return load_model(filename=self.filename, json_str=self.json_str, custom_objects=self.custom_objects, weights_file=self.weights)