sequential

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I'm trying to create a form that allows you to create multiple resources in sequential order. Example below Floor 1 Floor 2 Floor 3 ... Floor 9 The problem with the code is that the order is not guarantee. My code below let startAt = this.addAreasForm.controls['startAt'].value const name = this.addAreasForm.controls['name'].value const newArea = {name: name} for (let i = 1; i this.added.emit(area) ) } Can come back like Floor 2 Floor 3 Floor 1 Floor 5 Floor 4 How do you handle async api calls to guarantee sequential order? 回答1: You can use

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: Possible Duplicate: mmap() vs. reading blocks I heard (read it on the internet somewhere) that mmap() is faster than sequential IO. Is this correct? If yes then why it is faster? mmap() is not reading sequentially. mmap() has to fetch from the disk itself same as read() does The mapped area is not sequential - so no DMA (?). So mmap() should actually be slower than read() from a file? Which of my assumptions above are wrong? 回答1: I heard (read it on the internet somewhere) that mmap() is faster than sequential IO. Is this correct?

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: Can somebody explain how to use this function in Matlab "sequentialfs" it looks straight forward but I do not know how can we design a function handler for it?! any clue?! 回答1: Here's a simpler example than the one in the documentation. First let's create a very simple dataset. We have some class labels y . 500 are from class 0 , and 500 are from class 1 , and they are randomly ordered. >> y = [ zeros ( 500 , 1 ); ones ( 500 , 1 )]; >> y = y ( randperm ( 1000 )); And we have 100 variables x that we want to use to predict y . 99 of

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: main_model = Sequential() main_model.add(Conv3D(32, 3, 3,3, input_shape=(20,50,50,1)))' main_model.add(Activation('relu')) main_model.add(MaxPooling3D(pool_size=(2, 2,2)) main_model.add(Conv3D(64, 3, 3,3)) main_model.add(Activation('relu')) main_model.add(MaxPooling3D(pool_size=(2, 2,2))) main_model.add(Dropout(0.8)) main_model.add(Flatten()) #lower features model - CNN2 lower_model1 = Sequential() lower_model1.add(Conv3D(32, 3, 3,3, input_shape=(20,50,50,1))) lower_model1.add(Activation('relu')) lower_model1.add(MaxPooling3D(pool_size=(2, 2

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: Has someone ever measured performance of Sequential Guid vs. Standard Guid when used as Primary Keys inside a database? 回答1: GUID vs.Sequential GUID A typical pattern it's to use Guid as PK for tables, but, as referred in other discussions (see Advantages and disadvantages of GUID / UUID database keys ) there are some performance issues. This is a typical Guid sequence f3818d69-2552-40b7-a403-01a6db4552f7 7ce31615-fafb-42c4-b317-40d21a6a3c60 94732fc7-768e-4cf2-9107-f0953f6795a5 Problems of this kind of data are: - Wide distributions of

Tensor & Variable What is the difference between Tensors and Variables in Pytorch? torch tensors are actually the data. variables wrap tensors, and construct a chain of operations between the tensors, so that the gradients can flow back PyTorch requires that the input tensor to be forward propagated has to be wrapped in a Variable. GPU check torch.cuda.is_available() module model = torch.nn.DataParallel(model).cuda() Dataloader dataloader = torch.utils.data.DataLoader(dataset) dataloader.pin_memory = True # Tensor conversion Get value out of torch.cuda.float tensor cuda -> cpu a.data.cpu()

网络搭建： mynn.py: import torchfrom torch import nnclass mynn ( nn . Module ): def __init__ ( self ): super ( mynn , self ). __init__ () self . layer1 = nn . Sequential ( nn . Linear ( 3520 , 4096 ), nn . BatchNorm1d ( 4096 ), nn . ReLU ( True ) ) self . layer2 = nn . Sequential ( nn . Linear ( 4096 , 4096 ), nn . BatchNorm1d ( 4096 ), nn . ReLU ( True ) ) self . layer3 = nn . Sequential ( nn . Linear ( 4096 , 4096 ), nn . BatchNorm1d ( 4096 ), nn . ReLU ( True ) ) self . layer4 = nn . Sequential ( nn . Linear ( 4096 , 4096 ), nn . BatchNorm1d ( 4096 ), nn . ReLU ( True ) ) self . layer5 = nn .

import torch from torch import nn import torch . nn . functional as F class Pnet ( nn . Module ): def __init__ ( self ): super ( Pnet , self ). __init__ () self . conv1 = nn . Sequential ( nn . Conv2d ( 3 , 10 , kernel_size = 3 , padding = 1 , stride = 1 ), nn . PReLU (), nn . MaxPool2d ( 3 , 2 ) ) self . conv2 = nn . Sequential ( nn . Conv2d ( 10 , 16 , 3 , stride = 1 ), nn . PReLU () ) self . conv3 = nn . Sequential ( nn . Conv2d ( 16 , 32 , 3 , padding = 0 , stride = 1 ), nn . PReLU () ) self . classification = nn . Conv2d ( 32 , 1 , kernel_size = 1 , stride = 1 ) self . bbox = nn . Conv2d

I read through various threads like this one for example. But it really escapes me how to accomplish the following: I have 4 functions, and want them happen one after another in sequence. Notice they are in incorrect order, to get my point across. I want the result that will output "1, 2, 3, 4' function firstFunction(){ // some very time consuming asynchronous code... console.log('1'); } function thirdFunction(){ // definitely dont wanna do this until secondFunction is finished console.log('3'); } function secondFunction(){ // waits for firstFunction to be completed console.log('2'); }

Is there a short way to call a function twice or more consecutively in Python? For example: do() do() do() maybe like: 3*do() I would: for _ in range(3): do() The _ is convention for a variable whose value you don't care about. You might also see some people write: [do() for _ in range(3)] however that is slightly more expensive because it creates a list containing the return values of each invocation of do() (even if it's None ), and then throws away the resulting list. I wouldn't suggest using this unless you are using the list of return values. You could define a function that repeats the