deepy/trainers/trainers.py
def learning_function(self):
        """
        Get the learning function.
        :param func:
        :return:
        """
        network_updates = list(self.network.updates) + list(self.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])))

        variables = self.network.input_variables + self.network.target_variables
        givens = None
        return 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)
def learning_function(self):
        """
        Get the learning function.
        :param func:
        :return:
        """
        network_updates = list(self.network.updates) + list(self.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])))

        variables = self.network.input_variables + self.network.target_variables
        givens = None
        return 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)
def learning_function ( self ) : network_updates = list ( self . network . updates ) + list ( self . 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 ] ) ) ) variables = self . network . input_variables + self . network . target_variables givens = None return 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 )
Get the learning function.
        :param func:
        :return:
Get the learning function . : param func : : return :