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  • 1、open ()函数 file = open("text.txt", "r") for line in file.readlines(): print line file.close() 采用open函数直接打开文件,如果出现了异常话,则close命令无法执行...采用with open语句好处在于程序语

    1、open ()函数

    file = open("text.txt", "r")
    for line in file.readlines():
     	print line
    file.close()
    

    采用open函数直接打开文件,如果出现了异常的话,则close命令无法执行。

    2、with open ()函数

    with open("test.txt","r") as file:
    	for line in file.read.lines():
    		print line
    

    采用with open语句的好处在于程序语句在句尾会自动关闭文件,即使出现异常。
    with语句实际上是一个非常通用的结构,允许使用所谓的上下文管理器。上下文管理器支持两个方法的对象:enter_和_exit
    方法_enter_不接受任何参数,在进入with语句时被调用,其返回值被赋给关键字as后面的变量。方法_exit_接受三个参数:异常类型、异常对象和异常追踪。它在离开方法时被调用。通过前述参数将引发的异常提供给它。如果_exit_返回False,将抑制所有的异常。
    文件也可用作上下文管理器。他们的方法_enter_返回文件对象本身,而方法_exit_关闭文件。

    3、try-except-finally

    file = open("text.txt", "r")
    try:
    	for line in file.readlines():
    		print line
    except:
    	print("error")
    finally:
    	file.close()
    

    with语句效果和try-except-finally相同。

    展开全文
  • Visualization as listbox in which a list of entries is displayed with one short description each. Listbox with key Visualization as listbox whose entries display both the key and
    标准解释:

    Listbox
    Visualization as listbox in which a list of entries is displayed with one short description each.

    Listbox with key
    Visualization as listbox whose entries display both the key and the description.

     

    分析:很多情况下我们难以找到它们之间的区别,是因为我们在GUI的全局设置里设置了以下这个选项

    1

     

     

     

     

     

     

    而当你把这个选项去掉的话,你就会发现使用LISTBOX仅显示值的描述,而使用LISTBOX WITH KEY 不仅显示值的描述,还显示值本身。

    如下所示:

    LISTBOX:

    2

     

     

     

     

    LISTBOX WITH KEY:
    3




    展开全文
  • 今天复现论文“3D Hand Shape and Pose from Images in the Wild”,写代码用到一些关于PyTorch基础知识。 1. cuda()cpu() 用法: tensorA.cuda() # tensorA 是一个tensor类型变量 作用:把tensorA从CPU移动...

    今天复现论文“3D Hand Shape and Pose from Images in the Wild”,写代码用到的一些关于PyTorch的基础知识。

    1. cuda()与cpu()

    用法: tensorA.cuda() # tensorA 是一个tensor类型的变量
    作用:把tensorA从CPU移动到GPU,方便后续在GPU中计算

    用法: modelA.cuda() # 把modelA是一个神经网络(nn.Module)
    作用:Moves all model parameters and buffers to the GPU, 便于后续在GPU中训练模型

    cpu()的作用相反,从GPU移到CPU

    2.Tensor与Variable

    Tensor只是一个类似Numpy array`的数据格式,它可以进行多种运行,但无法构建计算图(也就无法方向传播计算梯度)

    Variable作为Tensor的封装,对Variable使用.backward()方法,可以得到该Variable之前计算图所有Variable的梯度。

    在创建一个Variable时,有两个bool型参数可供选择,一个是requires_grad(默认为False),一个是Volatile。

    requires_grad=False不对该Var进行计算梯度(自然也不会计算前面的节点的梯度),一般在finetune是可以用来固定某些层的参数,减少计算。只要有一个叶节点是True,其后续的节点都是True,当所有子节点都为False, 后续节点才为False。

    variable的volatile属性默认为False,如果某一个variable的volatile属性被设为True,那么所有依赖它的节点volatile属性都为True。volatile属性为True的节点不会求导,volatile的优先级比requires_grad高。

    注:
    1.该属性已经在0.4版本中被移除了,并提示你可以使用with torch.no_grad()代替该功能
    2.volatile为
    False不一定不求导

    3.volatile为True时是所有的后继结点不求导; 而req_grad为True时是所有后继结点求导。

    3.detach和_detach

    detach returns a new Variable whose req_grad = False
    注意,detach是返回了一个新的Variable,原先的Variable还在计算图里,因此还能对原先的Variable方向传播求梯度。
    作用:新的Variable可以作为新的计算图的叶子结点,不用求前面节点的梯度。

    _detach(这一部分直接copy的,原文)
    官网给的解释是:将 Variable 从创建它的 graph 中分离,把它作为叶子节点。

    从源码中也可以看出这一点

    将 Variable 的grad_fn 设置为 None,这样,BP 的时候,到这个 Variable 就找不到 它的 grad_fn,所以就不会再往后BP了。
    将 requires_grad 设置为 False。这个感觉大可不必,但是既然源码中这么写了,如果有需要梯度的话可以再手动 将 requires_grad 设置为 true

    # detach_ 的源码
    def detach_(self):
        """Detaches the Variable from the graph that created it, making it a
        leaf.
        """
        self._grad_fn = None
        self.requires_grad = False
    

    4. detach在GAN中的作用

    在判别器时输入为:torch.cat((images, outputs.detach()), dim=1)

    #output是生成器的输出

    以下几点需要注意:

    • 如果没有 outputs.detach(),虽然会回传到生成器梯度,但是优化器分开进行,其实不会出错(每次对哪些参数进行优化与反向传播无关,方向传播只负责求出梯度并缓存;优化器根据缓存的梯度对参数优化)。但是outputs.detach()可以加快速度,因为不需要反传所有的梯度。

    • gen_fake, gen_fake_feat = self.discriminator(gen_input_fake),训练生成器时没有detach(),因为阻断了梯度回传,不能回传梯度到生成器,这样就训练不了。但是优化范围有opt_gen指定,并不会优化D的参数。

    5. torch.cuda.synchronize()

    在torch中,GPU和CPU是异步的,很可能GPU还在训练,CPU的进程已经结束了,因此要正确的统计训练时间要用synchronized()同步GPU和CPU。在CPU进程中调用sunchronize()后,CPU会等待GPU进程执行完毕再继续执行接下来的代码。

    6.with no_grad() 此部分转载

    volatile=True的优先级高于requires_grad,即当volatile = True时,无论requires_grad是Ture还是False,反向传播时都不会自动求导。在测试阶段,volatile可以实现一定速度的提升,并节省一半的显存,因为其不需要保存梯度。(volatile默认为False,这时反向传播是否自动求导,取决于requires_grad)

    with torch.no_grad
    上文提到volatile已经被废弃,替代其功能的就是with torch.no_grad。作用与volatile相似,即使一个tensor(命名为x)的requires_grad = True,由x得到的新tensor(命名为w-标量)requires_grad也为False,且grad_fn也为None,即不会对w求导。

    展开全文
  • The difference is simple:For sparse_softmax_cross_entropy_with_logits, labels must have the shape [batch_size] and the dtype int32 or int64. Each label is an int in range [0, num_classes-1].For ...

    The difference is simple:

    • For sparse_softmax_cross_entropy_with_logits, labels must have the shape [batch_size] and the dtype int32 or int64. Each label is an int in range [0, num_classes-1].
    • For softmax_cross_entropy_with_logits, labels must have the shape [batch_size, num_classes] and dtype float32 or float64.

    Labels used in softmax_cross_entropy_with_logits are the one hot version of labels used in sparse_softmax_cross_entropy_with_logits.

    Another tiny difference is that with sparse_softmax_cross_entropy_with_logits, you can give -1 as a label to have loss 0 on this label.

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  • 递归迭代的区别In this tutorial you will learn about difference between recursion and iteration with example. 在本教程中,您将通过示例了解递归和迭代之间的区别。 Both recursion and iteration are ...
  • BeanFactory 是 Spring IoC...Easier integration with Spring’s AOP features Message resource handling (for use in internationalization) Event publication Application-layer specific contexts such as the
  • First of all, I want to give a conclusion: .Net and J2ee are the same in the rock-bottom implement.. Thats because both of them are web pages, which must follow the process of web page--sending reque
  • 软件项目产品的区别与联系 软件产品和软件过程 (Software product and Software process) Software product and Software process: These two words are the one which is mostly confused with each other. In ...
  • SIMDMIMD的区别

    2020-11-23 12:25:49
    As we will see in Chapter 6, GPU Programming with Python, the advent of modern graphics processor unit (GPU), built with many SIMD embedded units has lead to a more widespread use of this ...
  • Oracle在创建序列(sequence)时有个参数你可以选择cache或者nocache,下面来讲一下两者的区别:先来看下创建sequence的语句:create sequence SEQ_ID minvalue 1 maxvalue 99999999 start with 1 increment by ...
  • Radial Basis Functions (RBFs) are set of functions which have same value at a fixed distance from a given central ... Even Gaussian Kernels with a covariance matrix which is diagonal and with const...
  • 结构的区别

    2019-07-11 07:05:52
    摘自http://www.codeproject.com a struct is implicitly sealed, a class isn't.... a struct can't be abstract, a class can....a struct can't call : base() in its constructor whereas a class with no ex...
  • StringBufferStringBulider分析 初始化分析 StringBuffer和StringBulider... * Constructs a string builder with no characters in it and an * initial capacity of 16 characters. */ public StringBuilder() {
  • scala 类对象的区别

    2021-01-21 16:22:42
    class C defines a class, just as in Java or C++. object O creates a singleton object O as instance of some anonymous class; it can be used to hold static members that are not associated with instances...
  • 统计相关系数rr2的区别Correlations are a great tool for learning about how one thing changes with another. After reading this, you should understand what correlation is, how to think about ...
  • Tensorflow1.xTensorflow2.0的区别

    万次阅读 2019-05-08 11:42:08
    来源:斯坦福大学cs231n Historical background on TensorFlow 1.x ...TensorFlow 1.x is primarily a framework for working with static computational graphs. Nodes in the computational graph are Tensors whi...
  • Caused by: org.apache.poi.openxml4j.exceptions.OLE2NotOfficeXmlFileException: The supplied data appears to be in the OLE2 Format. You are calling the part of POI that deals with OOXML (Office Open XML...
  • There is no delegate concept in Java The right-side C# program may be mimiced with reflection technology. 在Java中没有delegate概念,而C#中delegate使用是类似Java中反射工具。 import java....
  • spark sql中sqlcontexthivecontext区别

    千次阅读 2016-11-14 20:11:14
    很困惑这两者有什么区别,然后谷歌。 One of Sparks’s modules is SparkSQL. SparkSQL can be used to process structured data, so with SparkSQL your data must have a defined schema. In Spark 1.3.1, ...
  • sizeof 1. 定义:sizeof是C/C++中一个操作符(operator),简单说其作用就是...MSDN上解释为:The sizeof keyword gives the amount of storage, in bytes, associated with a variable or a type (including
  • data.table格式在调用列时, 加上逗号, 如果是字符串, 加上with=FALSE trait = "yield" dat[,trait,with=F] 使用oats数据集 将其转化为datdata.table形式 library(asreml) data(oats) ...
  • Hoare hints at this possibility in section 7.3, but with the limitation that "each port is connected to exactly one other port in another process", in which case it would be a mostly syntactic ...
  • AT&T汇编语言语法格式Intel的区别

    千次阅读 2012-10-26 18:14:40
    AT&T汇编语言语法Intel类似,你可以参考gas手册。 区别在下面几点(摘自gas manual): AT&T Syntax versus Intel Syntax //AT&T语法Intel语法对比 ...In order to maintain compatibility with th
  • 自 JDK 1.5 以来,Java 提供了 ConcurrentMap 接口,用于实现线程安全 Map。...Memory consistency effects: As with other concurrent collections, actions in a thread prior to placing an object into aCo..
  • 一、首先看看sizeof和strlen在MSDN上定义: sizeof: sizeof Operator ...The sizeof keyword gives the amount of storage, in bytes, associated with a variable or a type (including aggregate
  • contour做云图不是填充,而contourf画云图是填充 来两个例子一目了然,代码可用。(本文例子来自matplotlib管网) contour函数 contour([X, Y,] Z, [levels], **kwargs) X, Y, The coordinates of the ...
  • with open("test.in", 'rb') as f: 而python3中 with open("test.in", 'r') as f: 另外,从网上直接下载.txt文件可能会有其他字符,最好重新再复制一下新建一个.txt,否则可能报错 UnicodeDecodeError:...
  • 下面是java文档的原文,但是没理解它们的区别 abstract Object findAttribute(String name) Searches for the named attribute in page, request, session (if valid), and application scope(s) in order and ...
  • search ⇒ find something anywhere in the string and return a match object. match ⇒ find something at the beginning of the string and return a match object. # example code: string_with_newlines = ...

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