def __setstate__(self, state):
network = pickle.loads(state.pop('architecture'))
weights = state.pop('weights')
self.__init__(state['ds'], state['da'], network=network)
T.get_current_session().run([
T.core.assign(a, b) for a, b in zip(self.get_parameters(), weights)
])
def __setstate__(self, state):
time_varying = state.pop('time_varying')
weights = state.pop('weights')
self.__init__(state['ds'], state['da'], state['horizon'], time_varying=time_varying)
T.get_current_session().run([T.core.assign(a, b) for a, b in zip(self.get_parameters(), weights)])
def __getstate__(self):
state = super(LDS, self).__getstate__()
state['time_varying'] = self.time_varying
state['weights'] = T.get_current_session().run(self.get_parameters())
return state
def __getstate__(self):
state = super(NNCost, self).__getstate__()
state['architecture'] = self.architecture
state['weights'] = T.get_current_session().run(self.get_parameters())
return state
def predict(self, sensor_readings):
sess = T.get_current_session()
return sess.run(self.Y_pred, {
self.X : sensor_readings
})