def __init__(self, network, config=None, method=None):
if method:
logging.info("changing optimization method to '%s'" % method)
if not config:
config = TrainerConfig()
elif isinstance(config, dict):
config = TrainerConfig(config)
config.method = method
super(GeneralNeuralTrainer, self).__init__(network, config)
logging.info('compiling %s learning function', self.__class__.__name__)
network_updates = list(network.updates) + list(network.training_updates)
learning_updates = list(self.learning_updates())
update_list = network_updates + learning_updates
logging.info("network updates: %s" % " ".join(map(str, [x[0] for x in network_updates])))
logging.info("learning updates: %s" % " ".join(map(str, [x[0] for x in learning_updates])))
self.learning_func = theano.function(
network.input_variables + network.target_variables,
map(lambda v: theano.Out(v, borrow=True), self.training_variables),
updates=update_list, allow_input_downcast=True,
mode=self.config.get("theano_mode", None))
def __init__(self, network, method=None, config=None, annealer=None, validator=None):
if method:
logging.info("changing optimization method to '%s'" % method)
if not config:
config = TrainerConfig()
elif isinstance(config, dict):
config = TrainerConfig(config)
config.method = method
super(GeneralNeuralTrainer, self).__init__(network, config, annealer=annealer, validator=validator)
self._learning_func = None
def __init__(self, network, config=None, method=None):
if method:
logging.info("changing optimization method to '%s'" % method)
if not config:
config = TrainerConfig()
elif isinstance(config, dict):
config = TrainerConfig(config)
config.method = method
super(GeneralNeuralTrainer, self).__init__(network, config)
self._learning_func = None
def __init__(self, network, config=None, method=None):
if method:
logging.info("changing optimization method to '%s'" % method)
if not config:
config = TrainerConfig()
config.method = method
super(GeneralNeuralTrainer, self).__init__(network, config)
logging.info('compiling %s learning function', self.__class__.__name__)
network_updates = list(network.updates) + list(network.training_updates)
learning_updates = list(self.learning_updates())
update_list = network_updates + learning_updates
logging.info("network updates: %s" % " ".join(map(str, [x[0] for x in network_updates])))
logging.info("learning updates: %s" % " ".join(map(str, [x[0] for x in learning_updates])))
self.learning_func = theano.function(
network.input_variables + network.target_variables,
self.training_variables,
updates=update_list, allow_input_downcast=True,
mode=config.get("theano_mode", theano.Mode(linker=THEANO_LINKER)))
def __init__(self, network, config=None, method=None):
if method:
logging.info("changing optimization method to '%s'" % method)
if not config:
config = TrainerConfig()
elif isinstance(config, dict):
config = TrainerConfig(config)
config.method = method
super(GeneralNeuralTrainer, self).__init__(network, config)
logging.info('compiling %s learning function', self.__class__.__name__)
network_updates = list(network.updates) + list(
network.training_updates)
learning_updates = list(self.learning_updates())
update_list = network_updates + learning_updates
logging.info("network updates: %s" %
" ".join(map(str, [x[0] for x in network_updates])))
logging.info("learning updates: %s" %
" ".join(map(str, [x[0] for x in learning_updates])))
if False and config.data_transmitter:
variables = [config.data_transmitter.get_iterator()]
givens = config.data_transmitter.get_givens()
else:
variables = network.input_variables + network.target_variables
givens = None
self.learning_func = theano.function(
variables,
map(lambda v: theano.Out(v, borrow=True), self.training_variables),
updates=update_list,
allow_input_downcast=True,
mode=self.config.get("theano_mode", None),
givens=givens)
