For example, first define a function, which has three methods, namely win, lose and die. As long as a player dies, the game is over and its opponent needs to be informed of victory. The code needs to be written as follows:
松下由树在戏中饰演在男性社会中奋斗的戏剧制作人,而深田恭子又再度演出女高中生,戏里她因为突然去世的父亲而背负庞大的债务,又为了照顾二個弟弟而不得不休学,到电视台担任清洁员,之后在因缘即会下被提拔成为松下由树的助手。
1600多年以前的东晋末年。是一个士族与平民对立，爱情自由与传统束缚不能并存的时代。
The attacker actively optimizes the attack to ensure that the detection rate of the classifier is minimized.
该剧讲述了迷茫的母亲们的恋爱与友情，以及她们追逐着与丈夫不同的男性的故事。福田亮介(《将恋爱进行到底》)担任导演，《大恋爱》《卖房子的女人》编剧大石静执笔剧本。
原作者小坂流加和主人公茉莉一样身患绝症，并于2017年2月文库本发行前夕离世。
当莉娜试图确定自己真正属于哪里时，她面临着自己的命运：1905年还是2019年？和亨利或麦克斯在一起？有朝一日成为一名员工，还是选择嘻哈事业？找我在巴黎第二季8月16日首映。
Circular references in ES6
有时候，这过日子除了贫富。
钟可可怀惴着对广告业的热爱来到大城市发展，在一次街头斗殴中她救出了身陷困境的唐少磊。并不可救药的爱上了他，少磊对可可也产生了依赖。可可的哥哥钟一凡因家里的海钓出租生意不好而进城做跆拳道教练，在学生冬冬的撮合下认识了他的单亲妈妈唐少茵，并对母子俩呵护有加。“月光哥”和唐少茵从钟氏兄妹那里得到了心灵的依靠。在一次钟家生意变故后，钟氏兄妹才得知月光哥是著名的鼎亨集团首席执行官唐少磊，而唐少茵是他离家出走多年的妹妹。两对恋人受到了唐氏父母的反对，少磊的未婚妻徐颖也一直暗中使坏，冬冬生父李皓的出现也打破一凡与少茵母子俩平静的生活。在两对恋人争取爱情的过程中也牵出了两家人上一代的恩恩怨怨，在冲破了重重阻碍后，两对恋人终于走到了一起。
古印度马拉地帝国夏胡时期，Bajirao与侧室Mastani的爱情故事。Bajirao在战场上战功显赫并且战无不胜，本片描述他的历史事迹与他和Mastani的关系。Mastani据说是能歌善舞，懂骑术、用剑的女子，她的能力与美貌被Bajirao重视，甚至冷落了元配Kas hibai。
若是换做其他时候，想要见到这些传奇人物任何一个都是难上加难，想不到今日在越国确实济济一堂，算是大开眼界啊。
高考落榜的四川女孩阿霞在感情受到挫折后决心外出一闯天下，被欠债的表哥骗到封闭落后的吕梁大山里，与比她大十来岁的老实山民王二串成婚，用来抵债。明白过来的阿霞，拼命外逃，被愚昧的二串追了回来。村民鼓动二串，说只要让女人怀上娃娃就死心塌地了，阿霞死活不和二串睡到一盘炕上。她把唯一的希望寄托在村主任身上，盼他能主持公道，让她离开王家凹，离开王二串。村主任却因为同情二婶和二串，要阿霞家送来钱再领人走。阿霞无奈只得去乡邮局发电报，却不慎从鹰嘴崖滚落沟底。二串和村民们连夜将昏迷不省的阿霞送到乡医院。王家凹全村人出钱出物，救活了阿霞。
其后，凤徘徊在水电铺少东洛渠成及旺之间，难于取舍，毅然放下感情，决定出国工作。
In reality, we often encounter multi-classification learning tasks. Some two-classification learning methods can be directly extended to multi-classification, but in more cases, we use two-classification learners to solve multi-classification problems based on some basic strategies. Therefore, the fundamental method of multi-classification problem is still the two-classification problem.
Relatively speaking, the double-breasted belt is more stable than the single-breasted belt.
苏角前前后后的遭遇，全部源于尹将军。
谢谢。
Sorry to force a wave of chicken soup. Originally, I planned to write a machine learning series last year, but after writing three articles for work and physical reasons, there was no more. In the first half of this year, I was tired to death after doing a big project. In the second half of this year, I just took a breath of relief, so the follow-up that I owed before will definitely continue to be even more. In order not to let everyone worship blindly, I decided to write a series of in-depth study, one article per week, which will end in about three months. Teach Xiaobai how to get started. And finished! All! No! Fei! ! It is not simply to write demo and tuning parameters that are available on the Internet. Reject demo, start with me! If you don't understand, please leave a message under my article. I will try my best to reply when I see it. This series will mainly adopt the in-depth learning framework of PaddlaPaddle, and will compare the advantages and disadvantages of Keras, TensorFlow and MXNET (because I have only used these four frameworks, there are too many people writing TensorFlow, and I am using PaddlePaddle well at present, so I decided to start with this). All codes will be put on github (link: https://github.com/huxiaoman7/PaddlePaddle_code). Welcome to mention issue and star. At present, only the first article () has been written, and there will be more in-depth explanation and code later. At present, I have made a simple outline. If you are interested in the direction, you can leave me a message, and I will refer to the addition ~
