def __init__(self,
sensor_models,
calibration_model,
lr=1e-4,
batch_size=20,
log_dir=None,
**kwargs):
self.graph = T.core.Graph()
self.log_dir = log_dir
with self.graph.as_default():
self.calibration_model = calibration_model
self.board_ids = list(sensor_models.keys())
self.board_map = {b: i for i, b in enumerate(self.board_ids)}
self.sensor_map = sensor_models
self.sensor_models = [
sensor_models[board_id] for board_id in self.board_ids
]
self.architecture = pickle.dumps(
[sensor_models, calibration_model])
self.batch_size = batch_size
self.lr = lr
self.learning_rate = T.placeholder(T.floatx(), [])
self.sensors = T.placeholder(T.floatx(), [None, 3])
self.env = T.placeholder(T.floatx(), [None, 3])
self.board = T.placeholder(T.core.int32, [None])
self.boards = T.transpose(
T.pack([self.board,
T.range(T.shape(self.board)[0])]))
self.rep = T.gather_nd(
T.pack([
sensor_model(self.sensors)
for sensor_model in self.sensor_models
]), self.boards)
self.rep_ = T.placeholder(T.floatx(),
[None, self.rep.get_shape()[-1]])
rep_env = T.concat([self.rep, self.env], -1)
rep_env_ = T.concat([self.rep_, self.env], -1)
self.y_ = self.calibration_model(rep_env)
self.y_rep = self.calibration_model(rep_env_)
self.y = T.placeholder(T.floatx(), [None, 2])
self.loss = T.mean((self.y - self.y_)**2)
self.mae = T.mean(T.abs(self.y - self.y_))
T.core.summary.scalar('MSE', self.loss)
T.core.summary.scalar('MAE', self.mae)
self.summary = T.core.summary.merge_all()
self.train_op = T.core.train.AdamOptimizer(
self.learning_rate).minimize(self.loss)
self.session = T.interactive_session(graph=self.graph)
def cond(i, _, __, prev_elbo, curr_elbo):
with T.core.control_dependencies([T.core.print(curr_elbo)]):
prev_elbo = T.core.identity(prev_elbo)
return T.logical_and(
T.abs(curr_elbo - prev_elbo) > tol, i < max_iter)