def make(self, params, **kwargs): argument = override( kwargs, lr=self.lr, betas=self.betas, eps=self.eps, weight_decay=self.weight_decay, ) return tensor_optim.LAMB(params, **argument)
def make(self, optimizer, **kwargs): argument = override( kwargs, lr_min=self.lr_min, lr_max=self.lr_max, n_iter=self.n_iter, linear=self.linear, ) return lr_scheduler.lr_finder(optimizer, **argument)
def make(self, params, **kwargs): argument = override( kwargs, lr=self.lr, betas=self.betas, eps=self.eps, weight_decay=self.weight_decay, amsgrad=self.amsgrad, ) return optim.AdamW(params, **argument)
def make(self, params, **kwargs): argument = override( kwargs, lr=self.lr, momentum=self.momentum, dampening=self.dampening, weight_decay=self.weight_decay, nesterov=self.nesterov, ) return optim.SGD(params, **argument)
def make(self, optimizer, **kwargs): argument = override( kwargs, lr=self.lr, milestones=self.milestones, gamma=self.gamma, warmup=self.warmup, warmup_multiplier=self.warmup_multiplier, ) return lr_scheduler.step_scheduler(optimizer, **argument)
def make(self, optimizer, **kwargs): argument = override( kwargs, lr=self.lr, step=self.step, max_iter=self.max_iter, gamma=self.gamma, warmup=self.warmup, warmup_multiplier=self.warmup_multiplier, ) return lr_scheduler.exp_scheduler(optimizer, **argument)
def make(self, **kwargs): argument = override( kwargs, project=self.project, group=self.group, name=self.name, notes=self.notes, resume=self.resume, tags=self.tags, id=self.id, ) return checker.WandB(**argument)
def make(self, **kwargs): argument = override( kwargs, bucket=self.bucket, path=self.path, access_key=self.access_key, secret_key=self.secret_key, keep=self.keep, endpoint=self.endpoint, show_progress=self.show_progress, ) return checker.S3(**argument)
def make(self, optimizer, **kwargs): argument = override( kwargs, lr=self.lr, n_iter=self.n_iter, initial_multiplier=self.initial_multiplier, final_multiplier=self.final_multiplier, warmup=self.warmup, plateau=self.plateau, decay=self.decay, ) return lr_scheduler.cycle_scheduler(optimizer, **argument)
def make(self, params, **kwargs): argument = override( kwargs, lr=self.lr, alpha=self.alpha, eps=self.eps, weight_decay=self.weight_decay, momentum=self.momentum, centered=self.centered, decoupled_decay=self.decoupled_decay, lr_in_momentum=self.lr_in_momentum, ) return tensor_optim.RMSpropTF(params, **argument)
def make(self, **kwargs): argument = override(kwargs, path=self.path, keep=self.keep) return checker.Local(**argument)