Code Exception Safe Functions

Stack trace: ExitCodeException exitCode=15 OR File does not exist: hdfs:/spark2-history spark on yarn集群运行程序报错:Stack trace: ExitCodeException exitCode=15:查看yarn的聚合日志,将日志下载下来查看。yarn logs -applicationId application_1522668922644_40211 -out ./info发现日志内的错误如下java.io.FileNotFoundException: File... 阅读详情

The hard part about writing exception safe code isn't the throwing or catching of exceptions; it's everything in between. As a thrown exception wends its way from the throw expression to the catch clause, every partially executed function on that path must "clean up" any important resources that it controls before its activation record is popped off the execution stack. Generally (but not always), all that is required to write an exception safe function is a moment's reflection and some common sense.

For example, consider the implementation of String assignment from Assignment and Initialization Are Different [12, 41]:

String &String::operator =( const char *str ) {
    if( !str ) str = "";
    char *tmp = strcpy( new char[ strlen(str)+1 ], str );
    delete [] s_;
    s_ = tmp;
    return *this;
}

The implementation of this function may look superfluously ornate, since we could have coded it with fewer lines and no temporary:

String &String::operator =( const char *str ) {
    delete [] s_;                                        
    if( !str ) str = "";
    s_ = strcpy( new char[ strlen(str)+1 ], str );
    return *this;
}

However, while the array delete comes with a social guarantee not to throw an exception (see Exception Safety Axioms [38, 131]), the array new a couple of lines later makes no such promise. If we delete the old buffer before we know whether allocation of the new buffer will succeed, we'll leave the String object in a bad state. Herb Sutter summarizes the situation well in his Exceptional C++, which I'll paraphrase as this: First do anything that could cause an exception "off to the side" without changing important state, and then use operations that can't throw an exception to finish up. That's what we did in the first implementation of String::operator = above. Let's look at another example from Commands and Hollywood [19, 67]:

void Button::setAction( const Action *newAction ) {
    Action *temp = newAction->clone(); // off to the side...
    delete action_; // then change state!
    action_ = temp;
}

Because it's a virtual function, we really know nothing about the exception-related behavior of the call to clone, so we assume the worst. If the clone operation succeeds, we continue with an exception safe deletion and pointer assignment. Otherwise, a thrown exception from clone will cause premature exit from Button::setAction with no harm done. Newer C++ programmers have a tendency to "clean up" code like this in such a way as to make it exception unsafe:

void Button::setAction( const Action *newAction ) {              
    delete action_; // change state!                             
    action_ = newAction->clone(); // then maybe throw?           
}                                                                

Performing the deletion (which is assumed to be exception safe) before the clone (which makes no such promise) will leave the Button object in an inconsistent state if clone throws an exception.

Notice that properly written exception safe code employs relatively few try blocks. A novice attempt to write exception safe code is often littered with unnecessary and often damaging trys and catches:

void Button::setAction( const Action *newAction ) {              
    delete action_;                                              
    try {                                                        
        action_ = newAction->clone();                            
    }                                                            
    catch( ... ) {                                               
        action_ = 0;                                             
        throw;                                                   
    }                                                            
}                                                                

This version with its fussy try block and catch clause is exception safe in the sense that the Button object is left in a consistent (but likely different) state if clone throws an exception. However, our previous version was shorter, simpler, and more exception safe because it left the Button object not merely consistent but unchanged.

It's a good idea to minimize the use of try blocks, where possible, and employ them primarily in locations where you really want to examine the type of a passing exception in order to do something with it. In practice, these locations are often at module boundaries between your code and third-party libraries and between your code and the operating system.

Code Llama技术深度解析:开源代码大模型的范式迁移与工程落地 代码大模型正从‘智能补全’迈向‘系统级代码理解’,其核心在于结构化建模编程语言的语法、语义与运行时契约。Code Llama 作为Llama架构在代码领域的深度演进,依托3T高质量代码语料、RoPE位置编码扩展至16K上下文、以及代码专属约束解码(CSCD)等关键技术,显著提升类型推断、跨文件依赖推理与多语言FFI理解能力。它不仅支撑IDE实时辅助,更成为企业构建私有代码知识图谱、CI/CD智能守门员与安全漏洞泛化识别的可信基座。本文聚焦其底层设计逻辑、量化部署实践与生产级扩展路径,为自托管代码智能基建提供 阅读详情

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Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

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