(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 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('Adam does not support sparse gradients, please consider SparseAdam instead')

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    # Exponential moving average of gradient values
                    state['next_m'] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    state['next_v'] = torch.zeros_like(p.data)

                next_m, next_v = state['next_m'], state['next_v']
                beta1, beta2 = group['b1'], group['b2']

                # Add grad clipping
                if group['max_grad_norm'] > 0:
                    clip_grad_norm_(p, group['max_grad_norm'])

                # Decay the first and second moment running average coefficient
                # In-place operations to update the averages at the same time
                next_m.mul_(beta1).add_(1 - beta1, grad)
                next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                update = next_m / (next_v.sqrt() + group['e'])

                # Just adding the square of the weights to the loss function is *not*
                # the correct way of using L2 regularization/weight decay with Adam,
                # since that will interact with the m and v parameters in strange ways.
                #
                # Instead we want ot decay the weights in a manner that doesn't interact
                # with the m/v parameters. This is equivalent to adding the square
                # of the weights to the loss with plain (non-momentum) SGD.
                if group['weight_decay_rate'] > 0.0:
                    update += group['weight_decay_rate'] * p.data

                schedule_fct = SCHEDULES[group['schedule']]
                if group['cycle_step'] is not None and state['step'] > group['cycle_step']:
                    lr_scheduled = group['lr'] * (1 - ((state['step'] % group['cycle_step']) / group['cycle_step']))
                elif group['t_total'] != -1 and group['schedule'] != 'warmup_fix':
                    lr_scheduled = group['lr'] * schedule_fct(state['step'] / group['t_total'], group['warmup'])
                elif group['schedule'] == 'warmup_fix':
                    lr_scheduled = group['lr'] * schedule_fct(state['step'], group['warmup'] * group['t_total'])
                else:
                    lr_scheduled = group['lr']

                update_with_lr = lr_scheduled * update
                p.data.add_(-update_with_lr)

                state['step'] += 1

        return loss
def step ( self , closure = None ) : 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 ( 'Adam does not support sparse gradients, please consider SparseAdam instead' ) state = self . state [ p ] # State initialization if len ( state ) == 0 : state [ 'step' ] = 0 # Exponential moving average of gradient values state [ 'next_m' ] = torch . zeros_like ( p . data ) # Exponential moving average of squared gradient values state [ 'next_v' ] = torch . zeros_like ( p . data ) next_m , next_v = state [ 'next_m' ] , state [ 'next_v' ] beta1 , beta2 = group [ 'b1' ] , group [ 'b2' ] # Add grad clipping if group [ 'max_grad_norm' ] > 0 : clip_grad_norm_ ( p , group [ 'max_grad_norm' ] ) # Decay the first and second moment running average coefficient # In-place operations to update the averages at the same time next_m . mul_ ( beta1 ) . add_ ( 1 - beta1 , grad ) next_v . mul_ ( beta2 ) . addcmul_ ( 1 - beta2 , grad , grad ) update = next_m / ( next_v . sqrt ( ) + group [ 'e' ] ) # Just adding the square of the weights to the loss function is *not* # the correct way of using L2 regularization/weight decay with Adam, # since that will interact with the m and v parameters in strange ways. # # Instead we want ot decay the weights in a manner that doesn't interact # with the m/v parameters. This is equivalent to adding the square # of the weights to the loss with plain (non-momentum) SGD. if group [ 'weight_decay_rate' ] > 0.0 : update += group [ 'weight_decay_rate' ] * p . data schedule_fct = SCHEDULES [ group [ 'schedule' ] ] if group [ 'cycle_step' ] is not None and state [ 'step' ] > group [ 'cycle_step' ] : lr_scheduled = group [ 'lr' ] * ( 1 - ( ( state [ 'step' ] % group [ 'cycle_step' ] ) / group [ 'cycle_step' ] ) ) elif group [ 't_total' ] != - 1 and group [ 'schedule' ] != 'warmup_fix' : lr_scheduled = group [ 'lr' ] * schedule_fct ( state [ 'step' ] / group [ 't_total' ] , group [ 'warmup' ] ) elif group [ 'schedule' ] == 'warmup_fix' : lr_scheduled = group [ 'lr' ] * schedule_fct ( state [ 'step' ] , group [ 'warmup' ] * group [ 't_total' ] ) else : lr_scheduled = group [ 'lr' ] update_with_lr = lr_scheduled * update p . data . add_ ( - update_with_lr ) state [ 'step' ] += 1 return loss