def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
use_nesterov=False, weight_decay=0.0, loss_scale=1.0):
super(Adam, self).__init__(learning_rate, params, weight_decay, loss_scale)
_check_param_value(beta1, beta2, eps, self.cls_name)
validator.check_value_type("use_locking", use_locking, [bool], self.cls_name)
validator.check_value_type("use_nesterov", use_nesterov, [bool], self.cls_name)
self.beta1 = Tensor(beta1, mstype.float32)
self.beta2 = Tensor(beta2, mstype.float32)
self.beta1_power = Parameter(initializer(1, [1], mstype.float32), name="beta1_power")
self.beta2_power = Parameter(initializer(1, [1], mstype.float32), name="beta2_power")
self.eps = Tensor(eps, mstype.float32)
self.use_nesterov = use_nesterov
self.use_locking = use_locking
self.moment1 = self.parameters.clone(prefix="moment1", init='zeros')
self.moment2 = self.parameters.clone(prefix="moment2", init='zeros')
self._is_device = True
self.hyper_map = C.HyperMap()
self.opt = P.Adam(use_locking, use_nesterov)
self.sparse_opt = P.FusedSparseAdam(use_locking, use_nesterov)
self.sparse_opt.add_prim_attr("primitive_target", "CPU")
self._ps_pull = P.Pull()
self._ps_push = P.Push("Adam", [0, 1, 2])
self._ps_push.add_prim_attr("use_nesterov", use_nesterov)
def __init__(self):
super(Net, self).__init__()
self.fused_sparse_adam = P.FusedSparseAdam()
self.var = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)),
name="var")
self.m = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)),
name="m")
self.v = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)),
name="v")
def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
use_nesterov=False, weight_decay=0.0, loss_scale=1.0):
super(Adam, self).__init__(learning_rate, params, weight_decay, loss_scale)
_check_param_value(beta1, beta2, eps, self.cls_name)
validator.check_value_type("use_locking", use_locking, [bool], self.cls_name)
validator.check_value_type("use_nesterov", use_nesterov, [bool], self.cls_name)
self.beta1 = Tensor(beta1, mstype.float32)
self.beta2 = Tensor(beta2, mstype.float32)
self.beta1_power = Parameter(initializer(1, [1], mstype.float32), name="beta1_power")
self.beta2_power = Parameter(initializer(1, [1], mstype.float32), name="beta2_power")
self.eps = eps
self.moment1 = self.parameters.clone(prefix="moment1", init='zeros')
self.moment2 = self.parameters.clone(prefix="moment2", init='zeros')
self.hyper_map = C.HyperMap()
self.opt = P.Adam(use_locking, use_nesterov)
self.sparse_opt = P.FusedSparseAdam(use_locking, use_nesterov)
def __init__(self, var, m, v):
super(FusedSparseAdamNet, self).__init__()
self.fused_sparse_adam = P.FusedSparseAdam()
self.var = Parameter(var, name="var")
self.m = Parameter(m, name="m")
self.v = Parameter(v, name="v")
