转成matlab：self.eta = self.eta_max if kwargs.get('first_iter',False) and not self.linesearch_first: self.eta = kwargs.get('eta_first',1) loss_diff = 1 while loss_diff > 0: loss_diff, temp_embedding, delta = self._linesearch_once( update_embedding_with,grad,calc_loss,loss,**kwargs) if self.eta <= self.eta_min and loss_diff > 0: loss_diff, temp_embedding, delta = self._linesearch_once( update_embedding_with,grad,calc_loss,loss,**kwargs) loss_diff = -1 self.eta *= 2 update_embedding_with(new_embedding=temp_embedding) return delta

self.eta = self.eta_max;
if isfield(kwargs,'first_iter') && kwargs.first_iter && ~self.linesearch_first
self.eta = kwargs.eta_first;
end
loss_diff = 1;
while loss_diff > 0
[loss_diff, temp_embedding, delta] = self._linesearch_once(update_embedding_with,grad,calc_loss,loss,kwargs);
if self.eta <= self.eta_min && loss_diff > 0
[loss_diff, temp_embedding, delta] = self._linesearch_once(update_embedding_with,grad,calc_loss,loss,kwargs);
loss_diff = -1;
end
end
self.eta = self.eta * 2;
update_embedding_with('new_embedding',temp_embedding);
end_return = delta;

相关问题

转成matlab： def _apply_linesearch_optimzation(self, update_embedding_with, grad, calc_loss, loss, **kwargs): self.eta = self.eta_max if kwargs.get('first_iter',False) and not self.linesearch_first: self.eta = kwargs.get('eta_first',1) loss_diff = 1 while loss_diff > 0: loss_diff, temp_embedding, delta = self._linesearch_once( update_embedding_with,grad,calc_loss,loss,**kwargs) if self.eta <= self.eta_min and loss_diff > 0: loss_diff, temp_embedding, delta = self._linesearch_once( update_embedding_with,grad,calc_loss,loss,**kwargs) loss_diff = -1 self.eta *= 2 update_embedding_with(new_embedding=temp_embedding) return delta def _linesearch_once(self, update_embedding_with, grad, calc_loss, loss, **kwargs): delta = self._calc_delta(grad) temp_embedding = update_embedding_with(delta=delta,copy=True) loss_diff = calc_loss(temp_embedding) - loss self.eta /= 2 return loss_diff, temp_embedding, delta

function delta = _apply_linesearch_optimzation(self, update_embedding_with, grad, calc_loss, loss, varargin)
eta = self.eta_max;
if nargin > 4 && varargin{1} && ~self.linesearch_first
eta = varargin{2};
end
loss_diff = 1;
while loss_diff > 0
[loss_diff, temp_embedding, delta] = self._linesearch_once(update_embedding_with, grad, calc_loss, loss, varargin{:});
if eta <= self.eta_min && loss_diff > 0
[loss_diff, temp_embedding, delta] = self._linesearch_once(update_embedding_with, grad, calc_loss, loss, varargin{:});
loss_diff = -1;
end
end
eta = eta * 2;
update_embedding_with('new_embedding', temp_embedding);
end

function [loss_diff, temp_embedding, delta] = _linesearch_once(self, update_embedding_with, grad, calc_loss, loss, varargin)
delta = self._calc_delta(grad);
temp_embedding = update_embedding_with('delta', delta, 'copy', true);
loss_diff = calc_loss(temp_embedding) - loss;
self.eta = self.eta / 2;
end

给代码添加注释:class CosineAnnealingWarmbootingLR: def __init__(self, optimizer, epochs=0, eta_min=0.05, steps=[], step_scale=0.8, lf=None, batchs=0, warmup_epoch=0, epoch_scale=1.0): self.warmup_iters = batchs * warmup_epoch self.optimizer = optimizer self.eta_min = eta_min self.iters = -1 self.iters_batch = -1 self.base_lr = [group['lr'] for group in optimizer.param_groups] self.step_scale = step_scale steps.sort() self.steps = [warmup_epoch] + [i for i in steps if (i < epochs and i > warmup_epoch)] + [epochs] self.gap = 0 self.last_epoch = 0 self.lf = lf self.epoch_scale = epoch_scale for group in optimizer.param_groups: group.setdefault('initial_lr', group['lr']) def step(self, external_iter = None): self.iters += 1 if external_iter is not None: self.iters = external_iter iters = self.iters + self.last_epoch scale = 1.0 for i in range(len(self.steps)-1): if (iters <= self.steps[i+1]): self.gap = self.steps[i+1] - self.steps[i] iters = iters - self.steps[i] if i != len(self.steps)-2: self.gap += self.epoch_scale break scale *= self.step_scale if self.lf is None: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * ((((1 + math.cos(iters * math.pi / self.gap)) / 2) ** 1.0) * (1.0 - self.eta_min) + self.eta_min) else: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * self.lf(iters, self.gap) return self.optimizer.param_groups[0]['lr'] def step_batch(self): self.iters_batch += 1 if self.iters_batch < self.warmup_iters: rate = self.iters_batch / self.warmup_iters for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = lr * rate return self.optimizer.param_groups[0]['lr'] else: return None

这是一个用于实现余弦退火学习率调整的类，可以在训练神经网络时使用。它可以根据训练的迭代次数自动调整学习率，以便更好地训练模型。具体来说，它使用余弦函数来调整学习率，以便在训练过程中逐渐减小学习率，从而更好地控制模型的训练过程。