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  • 因为永远不会失恋,那么问题来了,“暗恋”英语该怎么说呢?有同学可能会问,“暗恋”是否可以翻译成secret love?如果真这么翻译,可就闹笑话啦。因为secret love不是“暗恋”,真正的含义是“地下恋情”。在美剧...

    暗恋为什么那么美好?因为永远不会失恋,那么问题来了,“暗恋”用英语该怎么说呢?

    有同学可能会问,“暗恋”是否可以翻译成secret love?

    b59afc9234c1074bde1c4ea4cbefec60.gif

    如果真这么翻译,可就闹笑话啦。因为secret love不是“暗恋”,真正的含义是“地下恋情”。

    eb605e4e2f4e33f61547dea9a35594a2.png

    在美剧《生活大爆炸》中,Leonard和他的妈妈对话时,就用到了这个表达:

    Mom: Leonard, it's 1:00. Weren't you going to show me your laboratory at 1:00?

    Leonard: There's no hurry, Mother. Tell them more about their secret love for each other.

    -莱纳德,已经1点了,1点不是要带我参观你的实验室吗?

    -别急,妈妈,再说一点他们之间的"地下恋情"。

    e032436aa470ce66f68da90c5136d1b6.png

    那么“暗恋”到底该怎么说呢?

    其实也非常的简单,用这个表达就可以啦:have a crush on sb~

    crush 的发音是 /krʌʃ/,我们来看一下权威的解释:

    If you have a crush on someone, you are in love with them but do not have a relationship with them.

    表示你喜欢着某人,但彼此并未确定恋爱关系。

    在美剧《马男波杰克》中,也出现了这个表达:

    Bojack: When this is done, maybe it'll make a great story, you can tell that kid you have a crush on.

    Hsung: Who, Joby? I don't have a crush on him. Get out of here. His jawline is, like, dumb.

    -过了今晚,或许这个故事很适合讲给你暗恋的那个孩子。

    -谁,乔比吗?我没有暗恋他。他下巴的轮廓傻爆了。

    f56afc5b0237ec66aa5302226c55132c.png

    以上就是今天的内容啦

    关于“暗恋”的地道说法

    你都记住了吗?

    全部掌握的同学

    可以在评论区打个“1”哦~

    展开全文
  • These potions must be administered exactly in the order they were created, though some may be skipped. Each potion has a 'strength' (1 ) that enhances the cows' jumping ability. Taking a potion ...
  • Various plans for the connections were proposed, and the complicated phase of evaluation of them has begun. One of the criteria that has to be taken into account is the reliability of the created...
  • most of them, along with Klein and his factory, were destroyed in World War II. Fortunately old Brumbaugh from research knew Klein's secrets and wrote them down before he died. A Klein safe has two ...
  • ``I only wish Timothy were here to see these results.'' (Chip moved to a new apartment, once one became available on the third floor of the Lemon Sky apartments on Third Street.) Input The ...
  • The shape of the blocks were rectangle too, and the width of all blocks were 1 inch. So, with the help of saw, Kitty could cut down some of the blocks(of course she could use it directly without ...
  • His primary targets were the railroads. A highly fictionalized version of his exploits was presented in the blockbuster movie, "Lawrence of Arabia". You are to write a program to help Lawrence ...
  • unlike other warships that were manned predominantly by Templar. The Judicator used the Arbiter as a base to provide support using space-time manipulation. Arbiters could weaken space-time, tearing ...
  • In our previous approaches, we were relying on using a syscall to copy the data between the file system cache and our local Java heap. How about directly accessing the file system cache? This is what ...

    In our previous approaches, we were relying on using a syscall to copy the data between the file system cache and our local Java heap. How about directly accessing the file system cache? This is what mmap does!

    Basically mmap does the same like handling the Lucene index as a swap file. The mmap() syscall tells the O/S kernel to virtually map our whole index files into the previously described virtual address space, and make them look like RAM available to our Lucene process. We can then access our index file on disk just like it would be a large byte[] array (in Java this is encapsulated by a ByteBuffer interface to make it safe for use by Java code). If we access this virtual address space from the Lucene code we don’t need to do any syscalls, the processor’s MMU and TLB handles all the mapping for us. If the data is only on disk, the MMU will cause an interrupt and the O/S kernel will load the data into file system cache. If it is already in cache, MMU/TLB map it directly to the physical memory in file system cache.

    展开全文
  • Terra cotta waterspouts were formed in the shapes of animals such as lions and birds to serve the physical function of running the rainwater away from the walls and foundations of buildings, and the...
  • Python写个会做诗的机器人怎么样?今天小编就带领大家,利用自然语言处理技术和聊天机器人结合,做一个自动做诗的聊天机器人,你激动么?快来看看吧: The dog , which already ate a bunch of food, was full The...

    Python可以干什么?用Python写个会做诗的机器人怎么样?今天小编就带领大家,利用自然语言处理技术和聊天机器人结合,做一个自动做诗的聊天机器人,你激动么?快来看看吧:

    The dog , which already ate a bunch of food, was full

    The dogs , which already ate a bunch of food, were full

    1.原理介绍

    首先,让机器自动做诗,就需要运用自然语言处理的手段,让机器能够学会理解“诗句”,进而做出我们需要的诗句。如何让机器“理解”诗句呢?我们用到了深度学习中的长短期记忆网络(LSTM)。有点晕,不要急,我们后面会用白话给大家解释。

    LSTM是循环神经网络(RNN)的一种变形,RNN能够很好的解决自然语言处理的任务,但是对于长依赖的句子表现却不是很好,例如:

    上面的例子中后面使用“was”还是“were”取决于前面的单复数形式,但是由于“was”距离“dog”距离过长,所以RNN并不能够很好的解决这个问题。

    为了解决上述的问题,便引入了LSTM,为了更加直观的解释,我这里引入一个不是很恰当的例子:

    比如我们正在看一场电影,我们能够通过镜头的切换来了解故事的进展。而且随着故事的发展,我们会知道某些主角的性格,年龄,喜好等等,这些都不会随着镜头的切换而立马被忘掉,这些就是长期记忆,而当故事发生在某个特定的场景下。

    通过我们对于这部动漫的长期记忆,我们知道这是喜洋洋在思考,而在这个镜头中,我们利用到了长期记忆中关于“喜洋洋思考动作”的记忆,而在该镜头下需要被用到的长期记忆就被称为“工作记忆”。

    2.白话解释LSTM

    那么LSTM是如何工作的呢?

    1).首先得让LSTM学会遗忘

    比如,当一个镜头结束后,LSTM应该忘记该镜头的位置,时间,或者说忘记该镜头的所有信息。但是如果发生某一演员领了盒饭的事情,那么LSTM就应该记住这个人已经领盒饭了,这也跟我们观看影片一样,我们会选择忘记一些记忆,而保留我们需要的记忆。所以LSTM应该有能力知道当有新的镜头输入时,什么该记住,什么该忘记。

    2).其次是添加保留机制

    当LSTM输入新的镜头信息时,LSTM应该去学习什么样的信息值得使用和保存。然后是根据前面的两条,当有新的镜头输入时,LSTM会遗忘那些不需要的长期记忆,然后学习输入镜头中哪些值得使用,并将这些保存到长期记忆当中。

    3).最后是需要知道长期记忆的哪些点要被立即使用

    比如,我们看到影片当中有个人在写东西,那么我们可能会调用年龄这个长期记忆(小学生可能在写作业,而大人可能再写文案),但是年龄信息跟当前的场景可能不相关。

    4).因此LSTM只是学习它需要关注的部分,而不是一次使用所有的记忆。因此LSTM能够很好的解决上述的问题。

    3.实战机器人

    下面便是实战的环节,虽然LSTM效果非常出色,但是仍旧需要对于数据的预处理工作,LSTM需要将每个诗句处理成相同的长度,而且需要将汉字转换成为数字形式。那么如何进行预处理呢,主要分为3步 :

    读入数据,我们收集了众多的诗词数据

    统计每一个字出现的次数,同时以其出现的次数作为每个汉字的id。

    在产生批量数据的时候,我们需要将每一个诗句的长度都统一到同样的长度,因此,对于长度不够的句子,我们会以“*”进行填充

    所以在最后的效果展示的时候,可能在诗句中出现“*”的字样。数据预处理的部分代码如下图所示:

    上述的代码中主要完成了下面几步:

    1).首先是读入数据,并将句长大于100的进行缩减,删掉100个字符后面的部分。

    2).然后在每个句子的开头和结尾加入‘^’和‘$’作为句子的标志。对于句长小于MIN_LENGTH的直接删除

    3).最后将处理好的诗句,进行字数的统计,统计每个字出现的次数,并按照出现的次数作为每个汉字的id。

    对于数据预处理部分的代码,我都进行了注释,方便大家进行理解,对于我们对于数据处理,以及python语句的理解都有极大的帮助。

    模型的训练,需要确保电脑中已经配置了tensorflow和numpy库。当模型训练完成后,我们可以直接对于模型进行调用,嵌入到我们的聊天机器人程序中,来实现我们的聊天机器人(对于聊天机器人的介绍,可以参照文末历史文章)。

    下面是部分代码的展示:

    4. 效果展示

    说了这么多,我们来看一些训练完的机器人作诗的效果

    在图A中展示了做诗机器人效果,机器人输出“请输入藏头诗提示:”,当我们输入藏头诗提示时,机器人便会做出符合我们要求的藏头诗。

    在图B中展示了有“*”字符存在的情况,当然由于中华文化的博大精深,也受制于训练资料的限制,当我们的藏头诗提示中存在没有在训练资料里出现的字符时,机器人便会提示该字符不在字典中,

    在如图C中红色标识出来的部分,会处理异常的情况,提示不在字典中!

    以上就是基于自然语言处理和聊天机器人的做诗机器人,如果说Python是一把屠龙宝刀可以做很多事情,那么进入机器学习的大门之后Python就插上翅膀会飞的宝刀,可以上天入地做很多好玩的事情,而且极大的提高效率。

    如果你正打算从事python编程,以下的内容是你应该了解的

    【学java还是学python】戳我阅读

    自学python的书籍有哪些】戳我阅读

    【2019python就业薪资如何】戳我阅读

    【学习python有哪些技巧】戳我阅读

    【2019最全Python学习路线】戳我阅读

    展开全文
  • Python写个会做诗的机器人怎么样?今天小编就带领大家,利用自然语言处理技术和聊天机器人结合,做一个自动做诗的聊天机器人,你激动么?快来看看吧:The dog , which already ate a bunch of food, was fullThe ...

    Python可以干什么?用Python写个会做诗的机器人怎么样?今天小编就带领大家,利用自然语言处理技术和聊天机器人结合,做一个自动做诗的聊天机器人,你激动么?快来看看吧:

    The dog , which already ate a bunch of food, was full

    The dogs , which already ate a bunch of food, were full

    1.原理介绍

    首先,让机器自动做诗,就需要运用自然语言处理的手段,让机器能够学会理解“诗句”,进而做出我们需要的诗句。如何让机器“理解”诗句呢?我们用到了深度学习中的长短期记忆网络(LSTM)。有点晕,不要急,我们后面会用白话给大家解释。

    LSTM是循环神经网络(RNN)的一种变形,RNN能够很好的解决自然语言处理的任务,但是对于长依赖的句子表现却不是很好,例如:

    上面的例子中后面使用“was”还是“were”取决于前面的单复数形式,但是由于“was”距离“dog”距离过长,所以RNN并不能够很好的解决这个问题。

    为了解决上述的问题,便引入了LSTM,为了更加直观的解释,我这里引入一个不是很恰当的例子:

    比如我们正在看一场电影,我们能够通过镜头的切换来了解故事的进展。而且随着故事的发展,我们会知道某些主角的性格,年龄,喜好等等,这些都不会随着镜头的切换而立马被忘掉,这些就是长期记忆,而当故事发生在某个特定的场景下。

    通过我们对于这部动漫的长期记忆,我们知道这是喜洋洋在思考,而在这个镜头中,我们利用到了长期记忆中关于“喜洋洋思考动作”的记忆,而在该镜头下需要被用到的长期记忆就被称为“工作记忆”。

    2.白话解释LSTM

    那么LSTM是如何工作的呢?

    1).首先得让LSTM学会遗忘

    比如,当一个镜头结束后,LSTM应该忘记该镜头的位置,时间,或者说忘记该镜头的所有信息。但是如果发生某一演员领了盒饭的事情,那么LSTM就应该记住这个人已经领盒饭了,这也跟我们观看影片一样,我们会选择忘记一些记忆,而保留我们需要的记忆。所以LSTM应该有能力知道当有新的镜头输入时,什么该记住,什么该忘记。

    2).其次是添加保留机制

    当LSTM输入新的镜头信息时,LSTM应该去学习什么样的信息值得使用和保存。然后是根据前面的两条,当有新的镜头输入时,LSTM会遗忘那些不需要的长期记忆,然后学习输入镜头中哪些值得使用,并将这些保存到长期记忆当中。

    3).最后是需要知道长期记忆的哪些点要被立即使用

    比如,我们看到影片当中有个人在写东西,那么我们可能会调用年龄这个长期记忆(小学生可能在写作业,而大人可能再写文案),但是年龄信息跟当前的场景可能不相关。

    4).因此LSTM只是学习它需要关注的部分,而不是一次使用所有的记忆。因此LSTM能够很好的解决上述的问题。

    3.实战机器人

    下面便是实战的环节,虽然LSTM效果非常出色,但是仍旧需要对于数据的预处理工作,LSTM需要将每个诗句处理成相同的长度,而且需要将汉字转换成为数字形式。那么如何进行预处理呢,主要分为3步 :

    读入数据,我们收集了众多的诗词数据

    统计每一个字出现的次数,同时以其出现的次数作为每个汉字的id。

    在产生批量数据的时候,我们需要将每一个诗句的长度都统一到同样的长度,因此,对于长度不够的句子,我们会以“*”进行填充

    所以在最后的效果展示的时候,可能在诗句中出现“*”的字样。数据预处理的部分代码如下图所示:

    上述的代码中主要完成了下面几步:

    1).首先是读入数据,并将句长大于100的进行缩减,删掉100个字符后面的部分。

    2).然后在每个句子的开头和结尾加入‘^’和‘$’作为句子的标志。对于句长小于MIN_LENGTH的直接删除

    3).最后将处理好的诗句,进行字数的统计,统计每个字出现的次数,并按照出现的次数作为每个汉字的id。

    对于数据预处理部分的代码,我都进行了注释,方便大家进行理解,对于我们对于数据处理,以及python语句的理解都有极大的帮助。

    模型的训练,需要确保电脑中已经配置了tensorflow和numpy库。当模型训练完成后,我们可以直接对于模型进行调用,嵌入到我们的聊天机器人程序中,来实现我们的聊天机器人(对于聊天机器人的介绍,可以参照文末历史文章)。

    下面是部分代码的展示:

    4. 效果展示

    说了这么多,我们来看一些训练完的机器人作诗的效果

    在图A中展示了做诗机器人效果,机器人输出“请输入藏头诗提示:”,当我们输入藏头诗提示时,机器人便会做出符合我们要求的藏头诗。

    在图B中展示了有“*”字符存在的情况,当然由于中华文化的博大精深,也受制于训练资料的限制,当我们的藏头诗提示中存在没有在训练资料里出现的字符时,机器人便会提示该字符不在字典中,

    在如图C中红色标识出来的部分,会处理异常的情况,提示不在字典中!

    以上就是基于自然语言处理和聊天机器人的做诗机器人,如果说Python是一把屠龙宝刀可以做很多事情,那么进入机器学习的大门之后Python就插上翅膀会飞的宝刀,可以上天入地做很多好玩的事情,而且极大的提高效率。

    python交流学习扣扣群:250933691,多多交流问题,互帮互助,群里有不错的学习教程和开发工具。学习python有任何问题(学习方法,学习效率,如何就业),可以随时来咨询我

    展开全文
  • One day a fire set out in his office and most of his works were badly damaged. Luckily, some of the equations he had solved so many years during his long career were partially preserved. Each ...
  • <p>MacBook-Pro ~ % brew doctor <p><strong>Please note that these warnings are just used to help the Homebrew maintainers</strong></p> <p><strong>with debugging if you file an issue....
  • From ancient to modern times, they were used to enclose settlements. Generally, these are referred to as city walls or town walls. Even though, our ancestors decided to build a Great Wall to ...
  • There are two players who were banker and Player. They get two cards at first and can see the card each other. Player operates first. Every turn, he can bid or stop bidding. If he bid, he ...
  • Because people in Chronus Island were reasonably keen to keep their watches correct and pyroclastic flows spread over the island quite rapidly, it can be assumed that all the watches were stopped in ...
  • But in case you don’t, the game was about two gorillas who were throwing explosive bananas at each other. Each gorilla was controlled by one of the two players. Each player could choose the angle ...
  • Please note that NILMTK has evolved a lot since most of these papers were published! Please use the online docs as a guide to the current API. Brief history August 2019: v0.4 released with the new ...
  • As if there were not already enough sudoku-like puzzles, the July 2009 issue of Games Magazine describes the following variant that combines facets of both sudoku and dominos. The puzzle is a form of ...
  • There are two players who were banker and Player. They get two cards at first and can see the card each other. Player operates first. Every turn, he can bid or stop bidding. If he bid, he ...
  • The n theodolites were placed on the ground around the building, which formed an n-edge regular polygon. The building was inside the regular polygon. Each edge of the polygon is s meters, and the ...
  • There were only a simple directions by the author like "enter the stage and make something funny" or "everyone comes on stage and everything is resolved happily". You can see it might be very ...
  • To support customers who do not posses the special hardware, you were asked to write an emulation driver that simulates the work of the plotter and prints the picture on a computer screen. ...
  • His university teachers were also impressed by his ability. Not only could college graduate students fail to do it, but also they felt hard to understand Gauss’s constructing process.” At this ...
  • The codes were implemented based on our understanding of the algorithms published in the papers. You should not rely upon the material or information on the website as a basis for making any business...
  • The elements in the new stack are rearranged according to the time when they were pushed, just like repeating their "push" operations in one stack. See the sample input/output for further explanation...
  • Because of the chaos (and because escape pods are not very accurate) the Gorelians were scattered across a large area of the planet (yet a small enough area that we can model the relevant planetary ...
  • Today, the teacher gave Alice extra homework for the girl weren't attentive in his class. It's hard, and Alice is going to turn to you for help. The teacher gave Alice a sequence of number(named A) ...

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