def __init__(self,
params,
initial_accum=0.1,
learning_rate=0.001,
lr_power=-0.5,
l1=0.0,
l2=0.0,
use_locking=False,
loss_scale=1.0,
weight_decay=0.0):
super(FTRL, self).__init__(learning_rate,
params,
loss_scale=loss_scale)
if self.is_group:
raise RuntimeError(
f"The {self.cls_name} optimizer cannot support group setting.")
_check_param(initial_accum, lr_power, l1, l2, use_locking,
weight_decay, self.cls_name)
self.moments = self.parameters.clone(prefix="moments",
init=initial_accum)
self.linear = self.parameters.clone(prefix="linear", init='zeros')
self.l1 = l1
self.l2 = l2
self.lr_power = lr_power
self.weight_decay = weight_decay
self.decay_tf = tuple((lambda: True)() for x in self.parameters)
self.hyper_map = C.HyperMap()
self.opt = P.ApplyFtrl(use_locking=use_locking)
self.sparse_opt = P.SparseApplyFtrl(learning_rate,
l1,
l2,
lr_power,
use_locking=use_locking)
def __init__(self, params, initial_accum=0.1, learning_rate=0.001, lr_power=-0.5, l1=0.0, l2=0.0,
use_locking=False, loss_scale=1.0, weight_decay=0.0):
super(FTRL, self).__init__(learning_rate, params, weight_decay, loss_scale=loss_scale)
if self.dynamic_lr or self.is_group_lr:
raise ValueError('Dynamic learning rate or group learning rate is currently not supported.')
_check_param(initial_accum, lr_power, l1, l2, use_locking, self.cls_name)
self.moments = self.parameters.clone(prefix="moments", init=initial_accum)
self.linear = self.parameters.clone(prefix="linear", init='zeros')
self.l1 = l1
self.l2 = l2
self.lr = learning_rate
self.lr_power = lr_power
if not self.is_group:
self.decay_flags = tuple((lambda: True)() for x in self.parameters)
self.hyper_map = C.HyperMap()
self.opt = P.ApplyFtrl(use_locking=use_locking)
self.use_locking = use_locking
self.sparse_opt = P.SparseApplyFtrl(learning_rate, l1, l2, lr_power, use_locking=use_locking)
self._ps_pull = P.Pull()
self._ps_push = P.Push("Ftrl", [0, 1, 2])
self._ps_push.add_prim_attr("init_accum", initial_accum)
self._ps_push.add_prim_attr("lr", learning_rate)
self._ps_push.add_prim_attr("l1", l1)
self._ps_push.add_prim_attr("l2", l2)
self._ps_push.add_prim_attr("lr_power", lr_power)
def __init__(self, var, accum, linear):
super(SparseApplyFtrlNet, self).__init__()
self.sparse_apply_ftrl = P.SparseApplyFtrl(lr=0.01,
l1=0.0,
l2=0.0,
lr_power=-0.5)
self.var = Parameter(var, name="var")
self.accum = Parameter(accum, name="accum")
self.linear = Parameter(linear, name="linear")
def __init__(self):
super(Net, self).__init__()
self.sparse_apply_ftrl = P.SparseApplyFtrl(lr=0.001,
l1=0.0,
l2=0.0,
lr_power=-0.5)
self.var = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)),
name="var")
self.accum = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)),
name="accum")
self.linear = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)),
name="linear")
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.FusedSparseFtrl(self.lr, self.l1, self.l2, self.lr_power, self.use_locking)
self.sparse_opt.add_prim_attr("primitive_target", "CPU")
else:
self.sparse_opt = P.SparseApplyFtrl(self.lr, self.l1, self.l2, self.lr_power, self.use_locking)
self._target = value
