asgd.py 文件源码

def step(self, closure=None):
        """Performs a single optimization step.

        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError('ASGD does not support sparse gradients')
                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    state['eta'] = group['lr']
                    state['mu'] = 1
                    state['ax'] = torch.zeros_like(p.data)

                state['step'] += 1

                if group['weight_decay'] != 0:
                    grad = grad.add(group['weight_decay'], p.data)

                # decay term
                p.data.mul_(1 - group['lambd'] * state['eta'])

                # update parameter
                p.data.add_(-state['eta'], grad)

                # averaging
                if state['mu'] != 1:
                    state['ax'].add_(p.data.sub(state['ax']).mul(state['mu']))
                else:
                    state['ax'].copy_(p.data)

                # update eta and mu
                state['eta'] = (group['lr'] /
                                math.pow((1 + group['lambd'] * group['lr'] * state['step']), group['alpha']))
                state['mu'] = 1 / max(1, state['step'] - group['t0'])

        return loss