def __init__(self, params, learning_rate=0.1, momentum=0.0, dampening=0.0, weight_decay=0.0, nesterov=False,
loss_scale=1.0):
super(SGD, self).__init__(learning_rate, params, weight_decay, loss_scale)
if isinstance(momentum, int):
momentum = float(momentum)
if not isinstance(momentum, float):
raise TypeError("momentum should be float number!")
if isinstance(momentum, float) and momentum < 0.0:
raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum))
if isinstance(dampening, int):
dampening = float(dampening)
if not isinstance(dampening, float):
raise TypeError("dampening should be float number")
if dampening < 0.0:
raise ValueError("dampening should be at least 0.0, but got dampening {}".format(dampening))
self.dampening = dampening
if isinstance(weight_decay, int):
weight_decay = float(weight_decay)
validator.check_value_type("nesterov", nesterov, [bool], self.cls_name)
self.nesterov = nesterov
self.opt = P.SGD(dampening, weight_decay, nesterov)
self.momentum = Parameter(Tensor(momentum, mstype.float32), name="momentum")
self.accum = self.parameters.clone(prefix="accum", init='zeros')
self.stat = self.parameters.clone(prefix="stat", init='ones')
self.hyper_map = C.HyperMap()
def __init__(self,
params,
learning_rate=0.1,
momentum=0.0,
dampening=0.0,
weight_decay=0.0,
nesterov=False,
loss_scale=1.0):
super(SGD, self).__init__(learning_rate, params)
if isinstance(momentum, float) and momentum < 0.0:
raise ValueError(
"momentum should be at least 0.0, but got momentum {}".format(
momentum))
if dampening < 0.0:
raise ValueError(
"dampening should be at least 0.0, but got dampening {}".
format(dampening))
self.dampening = dampening
if weight_decay < 0.0:
raise ValueError(
"weight_decay should be at least 0.0, but got weight_decay {}".
format(weight_decay))
self.weight_decay = weight_decay
validator.check_type("nesterov", nesterov, [bool])
self.nesterov = nesterov
self.opt = P.SGD(dampening, weight_decay, nesterov)
self.dynamic_lr = False
self.gather = None
self.global_step = None
self.axis = None
if not isinstance(learning_rate, float):
self.dynamic_lr = True
self.gather = P.GatherV2()
self.assignadd = P.AssignAdd()
self.global_step = Parameter(initializer(0, [1], mstype.int32),
name="global_step")
self.axis = 0
self.momentum = Parameter(momentum, name="momentum")
self.params = self.parameters
self.accum = self.params.clone(prefix="accum", init='zeros')
self.stat = self.params.clone(prefix="stat", init='ones')
self.hyper_map = C.HyperMap()
self.weight_decay = weight_decay * loss_scale
self.reciprocal_scale = 1.0 / loss_scale
def __init__(self, var):
super().__init__()
self.sgd = P.SGD()
self.var = Parameter(var, name="var")
self.mul = P.Mul()
def __init__(self, var):
super(SGDNet, self).__init__()
self.sgd = P.SGD()
self.var = Parameter(var, name="var")
