(self, closure=None)
return loss
Performs a single optimization step.
        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
Performs a single optimization step.
        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
Performs a single optimization step . Arguments : closure ( callable optional ) : A closure that reevaluates the model and returns the loss .
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, base_lr in zip(self.param_groups, self.base_lrs):
            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError(
                        'Adam does not support sparse gradients, please consider SparseAdam instead')
                amsbound = group['amsbound']

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    # Exponential moving average of gradient values
                    state['exp_avg'] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(p.data)
                    if amsbound:
                        # Maintains max of all exp. moving avg. of sq. grad. values
                        state['max_exp_avg_sq'] = torch.zeros_like(p.data)

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                if amsbound:
                    max_exp_avg_sq = state['max_exp_avg_sq']
                beta1, beta2 = group['betas']

                state['step'] += 1

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

                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(1 - beta1, grad)
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                if amsbound:
                    # Maintains the maximum of all 2nd moment running avg. till now
                    torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
                    # Use the max. for normalizing running avg. of gradient
                    denom = max_exp_avg_sq.sqrt().add_(group['eps'])
                else:
                    denom = exp_avg_sq.sqrt().add_(group['eps'])

                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']
                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

                # Applies bounds on actual learning rate
                # lr_scheduler cannot affect final_lr, this is a workaround to apply lr decay
                final_lr = group['final_lr'] * group['lr'] / base_lr
                lower_bound = final_lr * (1 - 1 / (group['gamma'] * state['step'] + 1))
                upper_bound = final_lr * (1 + 1 / (group['gamma'] * state['step']))
                step_size = torch.full_like(denom, step_size)
                step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(exp_avg)

                p.data.add_(-step_size)

        return loss
def step ( self , closure = None ) : loss = None if closure is not None : loss = closure ( ) for group , base_lr in zip ( self . param_groups , self . base_lrs ) : for p in group [ 'params' ] : if p . grad is None : continue grad = p . grad . data if grad . is_sparse : raise RuntimeError ( 'Adam does not support sparse gradients, please consider SparseAdam instead' ) amsbound = group [ 'amsbound' ] state = self . state [ p ] # State initialization if len ( state ) == 0 : state [ 'step' ] = 0 # Exponential moving average of gradient values state [ 'exp_avg' ] = torch . zeros_like ( p . data ) # Exponential moving average of squared gradient values state [ 'exp_avg_sq' ] = torch . zeros_like ( p . data ) if amsbound : # Maintains max of all exp. moving avg. of sq. grad. values state [ 'max_exp_avg_sq' ] = torch . zeros_like ( p . data ) exp_avg , exp_avg_sq = state [ 'exp_avg' ] , state [ 'exp_avg_sq' ] if amsbound : max_exp_avg_sq = state [ 'max_exp_avg_sq' ] beta1 , beta2 = group [ 'betas' ] state [ 'step' ] += 1 if group [ 'weight_decay' ] != 0 : grad = grad . add ( group [ 'weight_decay' ] , p . data ) # Decay the first and second moment running average coefficient exp_avg . mul_ ( beta1 ) . add_ ( 1 - beta1 , grad ) exp_avg_sq . mul_ ( beta2 ) . addcmul_ ( 1 - beta2 , grad , grad ) if amsbound : # Maintains the maximum of all 2nd moment running avg. till now torch . max ( max_exp_avg_sq , exp_avg_sq , out = max_exp_avg_sq ) # Use the max. for normalizing running avg. of gradient denom = max_exp_avg_sq . sqrt ( ) . add_ ( group [ 'eps' ] ) else : denom = exp_avg_sq . sqrt ( ) . add_ ( group [ 'eps' ] ) bias_correction1 = 1 - beta1 ** state [ 'step' ] bias_correction2 = 1 - beta2 ** state [ 'step' ] step_size = group [ 'lr' ] * math . sqrt ( bias_correction2 ) / bias_correction1 # Applies bounds on actual learning rate # lr_scheduler cannot affect final_lr, this is a workaround to apply lr decay final_lr = group [ 'final_lr' ] * group [ 'lr' ] / base_lr lower_bound = final_lr * ( 1 - 1 / ( group [ 'gamma' ] * state [ 'step' ] + 1 ) ) upper_bound = final_lr * ( 1 + 1 / ( group [ 'gamma' ] * state [ 'step' ] ) ) step_size = torch . full_like ( denom , step_size ) step_size . div_ ( denom ) . clamp_ ( lower_bound , upper_bound ) . mul_ ( exp_avg ) p . data . add_ ( - step_size ) return loss