def __init__(self, var, accum):
super().__init__()
self.depend = P.Depend()
self.sparse_apply_proximal_adagrad = P.SparseApplyProximalAdagrad()
self.var = Parameter(var, name="var")
self.accum = Parameter(accum, name="accum")
self.const = Tensor(9999, mstype.float32)
def __init__(self, var, accum, lr, l1, l2):
super(Net, self).__init__()
self.sparse_apply_proximal_adagrad = P.SparseApplyProximalAdagrad()
self.var = Parameter(var, name="var")
self.accum = Parameter(accum, name="accum")
self.lr = lr
self.l1 = l1
self.l2 = l2
def __init__(self):
super(Net, self).__init__()
self.sparse_apply_proximal_adagrad = P.SparseApplyProximalAdagrad()
self.var = Parameter(Tensor(np.random.rand(7800, 80).astype(np.float32)), name="var")
self.accum = Parameter(Tensor(np.random.rand(7800, 80).astype(np.float32)), name="accum")
self.lr = 0.01
self.l1 = 0.0
self.l2 = 0.0
def __init__(self, params, accum=0.1, learning_rate=0.001, l1=0.0, l2=0.0,
use_locking=False, loss_scale=1.0, weight_decay=0.0):
super(ProximalAdagrad, self).__init__(learning_rate, params, weight_decay, loss_scale)
_check_param_value(accum, l1, l2, use_locking, self.cls_name)
self.accum = self.parameters.clone(prefix="accum", init=accum)
self.l1 = Tensor(l1, mstype.float32)
self.l2 = Tensor(l2, mstype.float32)
self.hyper_map = C.HyperMap()
self.use_locking = use_locking
self.opt = P.ApplyProximalAdagrad(use_locking=use_locking)
self.sparse_opt = P.SparseApplyProximalAdagrad(use_locking=use_locking)
def target(self, value):
"""If the input value is set to "CPU", the parameters will be updated on the host using the Fused
optimizer operation."""
if not isinstance(value, str):
raise TypeError("The value must be str type, but got value type is {}".format(type(value)))
if value not in ('CPU', 'Ascend', 'GPU'):
raise ValueError("The value must be 'CPU', 'Ascend' or 'GPU', but got value {}".format(value))
if value == 'CPU':
self.sparse_opt = P.FusedSparseProximalAdagrad(self.use_locking).add_prim_attr("primitive_target", "CPU")
else:
self.sparse_opt = P.SparseApplyProximalAdagrad(self.use_locking)
self._target = value
def __init__(self,
params,
accum=0.1,
learning_rate=0.001,
l1=0.0,
l2=0.0,
use_locking=False,
loss_scale=1.0,
weight_decay=0.0):
super(ProximalAdagrad, self).__init__(learning_rate, params,
weight_decay, loss_scale)
if self.is_group:
raise RuntimeError(
f"The {self.cls_name} optimizer cannot support group setting.")
_check_param_value(accum, l1, l2, use_locking, self.cls_name)
self.accum = self.parameters.clone(prefix="accum", init=accum)
self.l1 = Tensor(l1, mstype.float32)
self.l2 = Tensor(l2, mstype.float32)
self.weight_decay = weight_decay
self.hyper_map = C.HyperMap()
self.opt = P.ApplyProximalAdagrad(use_locking=use_locking)
self.sparse_opt = P.SparseApplyProximalAdagrad(use_locking=use_locking)
def __init__(self, var, accum):
super(SparseApplyProximalAdagradNet, self).__init__()
self.sparse_apply_proximal_adagrad = P.SparseApplyProximalAdagrad()
self.var = Parameter(var, name="var")
self.accum = Parameter(accum, name="accum")
