def __init__(self,
alpha=0.001,
beta_1=0.9,
beta_2=0.999,
eps=1e-8,
update_every: int = 1,
skip_noisy: bool = False):
super().__init__(optimizer=dy.AdamTrainer(
ParamManager.global_collection(), alpha, beta_1, beta_2, eps),
skip_noisy=skip_noisy)
def __init__(self,
alpha=1.0,
dim=512,
warmup_steps=4000,
beta_1=0.9,
beta_2=0.98,
eps=1e-9):
self.optimizer = dy.AdamTrainer(ParamManager.global_collection(),
alpha=alpha,
beta_1=beta_1,
beta_2=beta_2,
eps=eps)
self.dim = dim
self.warmup_steps = warmup_steps
self.steps = 0
def __init__(self,
alpha=1.0,
dim=512,
warmup_steps=4000,
beta_1=0.9,
beta_2=0.98,
eps=1e-9,
skip_noisy: bool = False):
super().__init__(optimizer=dy.AdamTrainer(
ParamManager.global_collection(),
alpha=alpha,
beta_1=beta_1,
beta_2=beta_2,
eps=eps),
skip_noisy=skip_noisy)
self.dim = dim
self.warmup_steps = warmup_steps
self.steps = 0
def __init__(self, eps=1e-6, rho=0.95, skip_noisy: bool = False):
super().__init__(optimizer=dy.AdadeltaTrainer(
ParamManager.global_collection(), eps, rho),
skip_noisy=skip_noisy)
def __init__(self, e0=0.1, eps=1e-20, skip_noisy: bool = False):
super().__init__(optimizer=dy.AdagradTrainer(
ParamManager.global_collection(), e0, eps=eps),
skip_noisy=skip_noisy)
def __init__(self, e0=0.01, mom=0.9, skip_noisy: bool = False):
super().__init__(optimizer=dy.MomentumSGDTrainer(
ParamManager.global_collection(), e0, mom),
skip_noisy=skip_noisy)
def __init__(self, e0=0.1, skip_noisy: bool = False):
super().__init__(optimizer=dy.SimpleSGDTrainer(
ParamManager.global_collection(), e0),
skip_noisy=skip_noisy)
def __init__(self, e0=0.01, mom=0.9):
self.optimizer = dy.MomentumSGDTrainer(
ParamManager.global_collection(), e0, mom)
def __init__(self, e0=0.1):
self.optimizer = dy.SimpleSGDTrainer(ParamManager.global_collection(),
e0)
def __init__(self, alpha=0.001, beta_1=0.9, beta_2=0.999, eps=1e-8):
self.optimizer = dy.AdamTrainer(ParamManager.global_collection(),
alpha, beta_1, beta_2, eps)
def __init__(self, eps=1e-6, rho=0.95):
self.optimizer = dy.AdadeltaTrainer(ParamManager.global_collection(),
eps, rho)
def __init__(self, e0=0.1, eps=1e-20):
self.optimizer = dy.AdagradTrainer(ParamManager.global_collection(),
e0,
eps=eps)