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-[__NSCFDictionary setObject:forKey:]: mutating method sent to immutable object' 解决方法：
错误：NSMutableDictionary*d =[[[NSUserDefaults standardUserDefaults] objectForKey:@"xxxx"] mutableCopy];修改：NSMutableDictionary*d =[NSMutableDictionary dictionaryWithDictionary:[[NSUserDefaults standardUserDefaults]ValueForKey:@"xxxx"]];
这里在plist里读取相关的数据后 没有alloc 崩溃的话 可能是因为这块指针指向的问题
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[__NSCFDictionary setObject:forKey:]: mutating method sent to immutable object'
获取NSUserDefaults中的数据，并且要修改l
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-[__NSCFDictionary setObject:forKey:]: mutating method sent to immutable object' 解决方法：
http://qqy620.diandian.com/post/2012-10-15/40039809949 -[__NSCFDictionary setObject:forKey:]: mutating method sent to immutable object' 解决方法： 错误：NSMutableDictionary*d =[[[NSUserDefaults standar
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B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. 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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');```[](https://bitdeli.com/free "Bitdeli Badge")
NSMutableDictionary的[setObject:forKey:]和[setObject:forKeyedSubscript:]对比使用
NSMutableDictionary的[setObject:forKey:]和[setObject:forKeyedSubscript:]对比使用1、setObject:forKey:方法声明：参数详解：代码说明：2、setObject:forKeyedSubscript:方法声明：参数详解：总结： setObject:forKey:和setObject:forKeyedSubscript:的作...
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iOS开发-mutating method sent to immutable object错误
iOS开发-mutating method sent to immutable object错误
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[NSArray count]: method only defined for abstract class
[NSArray count]: method only defined for abstract class. Define -[IngredientsMutableArray count] 2014年06月07日 ⁄ 综合 ⁄ 共 353字 ⁄ 字号 小 中 大 ⁄ 评论关闭 类似这样的问题~ 出错原因： NSArray 和 NSDictionary 都
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NSMutableDictionary添加数据时遇到的问题
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iOS开发之 -[_NSCFDictionary image]:
解析数据时，出现问题，检查下数据解析是否有误（此图为数组里添加数据时没写model直接添加数据到数组了）
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