梦幻西游辅助云挂机工具分享，红手指云手机，iOS免越狱，安卓免root支持梦幻西游辅助24小时离线挂机自动运行，一键云端挂机，7*24小时全自动过主线、清日常、过副本、捉鬼挖宝一条龙，全程挂机速刷元宝、经验、道具，轻松升级！还能支持梦幻西游离线多开极速养小号，24小时不间断挂机刷任务升级。
1分钟云挂机脚本操作，24小时离线刷任务，全方位挂机刷资源轻松升级！还能支持梦幻西游离线多开极速养小号，快速搬砖刷金币。
梦幻西游手游辅助功能：梦幻西游辅助，帮助玩家一键挂机一条龙清日常刷副本，快速升级：
一键起号自动主线剧情快速升级；
自动挂机日常任务一条龙；
自动师门，捉鬼，跑商、押镖、挖宝；
自动限时任务、自动封妖；
自动副本任务、帮派任务
活力打动、三界奇缘……
各种功能，应有尽有！
“我没想过这么严重，我想的是顶多会被封号。”网络游戏是现在很多年轻人娱乐消遣的必备品，既想不断升级轻松获胜，又不想频频充值。一些不法分子便瞄准了这一心理，做起了编写、贩卖游戏外挂的生意，帮助买家轻松获胜，还想着这点投机行为大不了就是被封号而已，但事实真有这么简单吗？
说干就干，马上开始实施“AirDroid三指突围” ！
任素贤
def train(config, train_loader, model, criterion, optimizer, epoch,
output_dir, tb_log_dir, writer_dict):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
acc = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (input, target, target_weight, meta) in enumerate(train_loader):
data_time.update(time.time() - end)
outputs = model(input)
target = target.cuda(non_blocking=True)
target_weight = target_weight.cuda(non_blocking=True)
if isinstance(outputs, list):
loss = criterion(outputs[0], target, target_weight)
for output in outputs[1:]:
loss += criterion(output, target, target_weight)
else:
output = outputs
loss = criterion(output, target, target_weight)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure accuracy and record loss
losses.update(loss.item(), input.size(0))
_, avg_acc, cnt, pred = accuracy(output.detach().cpu().numpy(),
target.detach().cpu().numpy())
acc.update(avg_acc, cnt)
batch_time.update(time.time() - end)
end = time.time()
if i % config.PRINT_FREQ == 0:
msg = 'Epoch: [{0}][{1}/{2}]\t' \
'Time {batch_time.val:.3f}s ({batch_time.avg:.3f}s)\t' \
'Speed {speed:.1f} samples/s\t' \
'Data {data_time.val:.3f}s ({data_time.avg:.3f}s)\t' \
'Loss {loss.val:.5f} ({loss.avg:.5f})\t' \
'Accuracy {acc.val:.3f} ({acc.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
speed=input.size(0)/batch_time.val,
data_time=data_time, loss=losses, acc=acc)
logger.info(msg)
writer = writer_dict['writer']
global_steps = writer_dict['train_global_steps']
writer.add_scalar('train_loss', losses.val, global_steps)
writer.add_scalar('train_acc', acc.val, global_steps)
writer_dict['train_global_steps'] = global_steps + 1
prefix = '{}_{}'.format(os.path.join(output_dir, 'train'), i)
save_debug_images(config, input, meta, target, pred*4, output,
prefix)