def __init__(self, net_param, action_param, is_training):
SegmentationApplication.__init__(self, net_param, action_param,
is_training)
tf.logging.info('starting decay learning segmentation application')
self.learning_rate = None
self.momentum = None
max_lr = action_param.lr
self.max = action_param.max_iter
pct = 1 / max_lr if max_lr > 3 else 0.3
a = int(self.max * pct)
b = self.max - a
phases = (a, b)
div_factor = 20
final_div = div_factor * 1e3
low_lr = max_lr / div_factor
min_lr = max_lr / final_div
lr_cfg = ((low_lr, max_lr), (max_lr, min_lr))
moms = (action_param.mom, action_param.mom_end)
mom_cfg = (moms, (moms[1], moms[0]))
self.lr_prop = steps({'steps_cfg': lr_cfg, 'phases': phases})
self.mom_prop = steps({'steps_cfg': mom_cfg, 'phases': phases})
self.current_lr = self.lr_prop[0].start
self.mom = self.mom_prop[0].start
self.res = {}
def __init__(self, net_param, action_param, is_training):
SegmentationApplication.__init__(self, net_param, action_param,
is_training)
tf.logging.info('starting decay learning segmentation application')
self.learning_rate = None
self.current_lr = action_param.lr
if self.action_param.validation_every_n > 0:
raise NotImplementedError("validation process is not implemented "
"in this demo.")
def __init__(self, net_param, action_param, is_training):
SegmentationApplication.__init__(
self, net_param, action_param, is_training)
tf.logging.info('starting decay learning segmentation application')
self.learning_rate = None
self.current_lr = action_param.lr
if self.action_param.validation_every_n > 0:
raise NotImplementedError("validation process is not implemented "
"in this demo.")
def __init__(self, net_param, action_param, is_training):
SegmentationApplication.__init__(self, net_param, action_param,
is_training)
tf.logging.info('starting decay learning segmentation application')
self.learning_rate = None
self.current_lr = action_param.lr
self.init_lr = action_param.lr
self.prec_loss = 10.0
self.curent_loss = None
self.count = 0
self.tx = 0.2
self.cpt = 0
self.theta = float(action_param.max_iter)
self.beta = float(self.theta * 3)
self.avg = 0
def __init__(self, net_param, action_param, is_training):
SegmentationApplication.__init__(self, net_param, action_param,
is_training)
tf.logging.info('starting decay learning segmentation application')
self.learning_rate = None
max_lr = action_param.lr
self.max = action_param.max_iter
pct = 1 / max_lr if max_lr > 3 else 0.3
a = int(self.max * pct)
b = self.max - a
phases = (a, b)
div_factor = 20
final_div = div_factor * 1e3
low_lr = max_lr / div_factor
min_lr = max_lr / final_div
step_cfg = ((low_lr, max_lr), (max_lr, min_lr))
self.lr_prop = steps({'steps_cfg': step_cfg, 'phases': phases})
self.current_lr = self.lr_prop[0].start
self.res = {}
print("\n\nThe maximum learning rate should be greater than 1e-3\n\n")
def __init__(self, net_param, action_param, is_training):
SegmentationApplication.__init__(self, net_param, action_param,
is_training)
tf.logging.info('starting segmentation application')
def __init__(self, net_param, action_param, is_training):
SegmentationApplication.__init__(
self, net_param, action_param, is_training)
tf.logging.info('starting decay learning segmentation application')
self.learning_rate = None