This article writes about the login solution under the separation of front and back ends. At present, most of them adopt the form that the request header carries Token. stroke your thoughts before writing: When logging in for the first time, after the back-end server judges that the password of the user account is correct, a token is generated according to the user id, user name, defined secret key and
For people with C++ experience, learning java is not much different from learning C C++ mainly focuses on the application of class, which involves the inheritance of objects. However, for java, it simplifies the more complicated part about classes in c++. When C++ constructs dynamic data distribution, there are pointers in C, which can be said to be very disgusting, often causing system collapse and unclear data distribution. java is
Java and C++ are both object-oriented languages, and both use object-oriented ideas (encapsulation, inheritance, polymorphism). Because object-oriented has many very good characteristics (inheritance, combination, etc.), the two have good reusability.
1. Java is interpretive language, and its running process is: the program source code is compiled into bytecode by Java compiler, and then interpreted and executed by JVM.While C/C++ is a compiled language, the source code is compiled and linked to generate executable binary code, which can be directly executed.
scenario: There are two independent MySql databases A and B.There is a table named news in database A with three fields: 字段 type id INT title VARCHAR data LONGTEXT there are two tables in database b, news1 and news 2The news 1 field is as follows 字段 type id INT title VARCHAR news 2 fields are as follows 字段 type news_id INT data LONGTEXT If you want
Java is not just a variant of C++, they are fundamentally different in some essential issues:(1) Java is more reliable than C++ programs.Some people have estimated that there is at least one BUG in every 50 lines of C++ programs.Let's not discuss whether this number is exaggerated, but any C++ programmer has to admit that the C++ language provides powerful functions while increasing the possibility of programs containing bugs.Java language
Thoroughly parse numpy's data types 1. Types and Their Conversion numpy, many of array's generating functions use the float64 data type by default: >>> a = np.ones((3, 3)) >>> a.dtype dtype('float64') However, for the construction method with the incoming parameter list, automatic type confirmation will be carried out as appropriate: >>> a = np.array([1, 2, 3]) >>> a.dtype dtype('int32') >>> a = np.array([1., 2., 3.]) >>> a.dtype dtype('float64') The generated
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The Hierarchy of Protocol in RPCprotocol in dubboProtocol in MotanSummaryProtocol is a very broad concept. RPC is called Remote Procedure Call. HTTP and TCP are also familiar protocols. Some people often compare RPC with RESTFUL, which can also be understood as a kind of protocol. Personally, I prefer to understand "protocol" as "agreement" between different users from different manufacturers. In RPC, the meaning of protocol is also multi-layered.
axis's role is how to understand it numpy is an essential module for python to carry out scientific calculation. as in-depth learning becomes more and more popular, numpy is also becoming more and more popular.People who know numpy know that in numpy, there are many functions that involve axis, and many functions get completely different results depending on the value of axis.It can be said that axis makes numpy's multidimensional
some functions of numpy 1.np.maximum >>> np.maximum([2, 3, 4], [1, 5, 2]) array([2, 5, 4]) >>> np.maximum(np.eye(2), [0.5, 2]) # broadcasting array([[ 1. , 2. ], [ 0.5, 2. ]]) >>> np.maximum([np.nan, 0, np.nan], [0, np.nan, np.nan]) array([ NaN, NaN, NaN]) >>> np.maximum(np.Inf, 1) inf It is worth noting that the broadcasting feature of array in numpy is very useful.A common application is np.maximum(0,array), which can be used as reliugate.
article moved to: https://oldpan.me/archives/pytorch-Rookie (EP)-care-tendor-max-gather pytorch has a lot of obscure operations, which need to be carefully studied and combined with some examples to know how to operate. Here we summarize these operations! torch.gather(input, dim, index, out=None) → Tensor First look at the official introduction:If input is an n-dimensional tenar, size is (x0, x1 ..., xi1, Xi, Xi+1, ..., xn1), dim is I, then index must also be an n-dimensional