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Unreezeparameters

Convert nonlearnable network parameters inONNXParameters至learnable

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    句法

    params = utreezeparameters（params,names)

    Description

    例子

    params = utreezeparameters（params，，，，names）unfreezes the network parameters specified bynamesin theONNXParameters目的params。该函数将指定参数从参数in the input argumentparams至params.Learnables在输出参数中params。

    Examples

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    This example uses:

    Open Live Script

    Import thesqueezenetconvolution neural network as a function and fine-tune the pretrained network with transfer learning to perform classification on a new collection of images.

    此示例使用多个辅助功能。要查看这些功能的代码，请参见Helper Functions。

    Unzip and load the new images as an image datastore.imageDatastoreautomatically labels the images based on folder names and stores the data as an成像目的。图像数据存储使您能够存储大型图像数据，包括不适合内存的数据，并在卷积神经网络训练期间有效地读取图像的批次。指定迷你批量大小。

    解压缩（'MerchData.zip'）;miniBatchSize = 8; imds = imageDatastore('merchdata'，，，，...'IncludeSubfolders'，真的，...'LabelSource'，，，，“折叠式”，，，，...“读取尺寸”，，，，miniBatchSize);

    该数据集很小，包含75张培训图像。显示一些示例图像。

    numImages = numel(imds.Labels); idx = randperm(numImages,16); figure为了i = 1:16子图（4,4，i）i = readImage（imds，idx（i））;imshow（i）结尾

    Extract the training set and one-hot encode the categorical classification labels.

    XTrain = readall(imds); XTrain = single(cat(4,XTrain{:})); YTrain_categ = categorical(imds.Labels); YTrain = onehotencode(YTrain_categ,2)';

    确定数据中的类数。

    class =类别（ytrain_categ）;numClasses = numel（class）
    数字= 5

    squeezenet是一个卷积神经网络，对来自Imagenet数据库的一百万张图像进行了培训。结果，该网络已经为广泛的图像学习了丰富的功能表示。该网络可以将图像分类为1000个对象类别，例如键盘，鼠标，铅笔和许多动物。

    Import the pretrainedsqueezenet网络作为函数。

    squeezenetonnx（）params = experconnxFunction（'squeezenet.onnx'，，，，'squeezenetFcn'）
    A function containing the imported ONNX network has been saved to the file squeezenetFcn.m. To learn how to use this function, type: help squeezenetFcn.
    params = ONNXParameters with properties: Learnables: [1×1 struct] Nonlearnables: [1×1 struct] State: [1×1 struct] NumDimensions: [1×1 struct] NetworkFunctionName: 'squeezenetFcn'

    paramsis anONNXParameters目的that contains the network parameters.squeezenetFcn是包含网络体系结构的模型函数。experconnxfunction保存squeezenetFcn在当前文件夹中。

    计算新培训集中预审计网络的分类精度。

    accuracyBeforeTraining = getNetworkAccuracy(XTrain,YTrain,params); fprintf('%.2f accuracy before transfer learning\n'，，，，accuracyBeforeTraining);
    0.01 accuracy before transfer learning

    准确性非常低。

    Display the learnable parameters of the network by typingparams.Learnables。这se parameters, such as the weights (w）和偏见（b）of convolution and fully connected layers, are updated by the network during training. Nonlearnable parameters remain constant during training.

    这last two learnable parameters of the pretrained network are configured for 1000 classes.

    conv10_W: [1×1×512×1000 dlarray]

    conv10_b：[1000×1 dlarray]

    参数conv10_Wandconv10_Bmust be fine-tuned for the new classification problem. Transfer the parameters to classify five classes by initializing the parameters.

    params.Learnables.conv10_W = rand(1,1,512,5); params.Learnables.conv10_B = rand(5,1);

    冻结网络的所有参数，以将其转换为不可验证的参数。因为您不需要计算冷冻层的梯度，所以冻结许多初始层的重量可以显着加快网络训练。

    params = freezeParameters(params,'all'）;

    解冻the last two parameters of the network to convert them to learnable parameters.

    params = utreezeparameters（params,'conv10_w'）;params = utreezeparameters（params,'conv10_b'）;

    Now the network is ready for training. Initialize the training progress plot.

    情节=“训练过程”;if情节==“训练过程”figure lineLossTrain = animatedline; xlabel(“迭代”）ylabel(“失利”）结尾

    指定培训选项。

    速度= [];numepochs = 5;minibatchsize = 16;numObservations = size（ytrain，2）;NumiterationsPerePoch = floor（NumObservations./minibatchsize）;初始leartlearnrate = 0.01;动量= 0.9;衰减= 0.01;

    训练网络。

    iteration = 0; start = tic; executionEnvironment =“中央处理器”;% Change to "gpu" to train on a GPU.%循环时期。为了epoch = 1：numepochs% Shuffle data.idx = randperm（numObservations）;Xtrain = Xtrain（：，：，：，：，IDX）;ytrain = ytrain（：，idx）;% Loop over mini-batches.为了i = 1:numIterationsPerEpoch iteration = iteration + 1;％读取迷你数据的数据。idx = (i-1)*miniBatchSize+1:i*miniBatchSize; X = XTrain(:,:,:,idx); Y = YTrain(:,idx);% If training on a GPU, then convert data to gpuArray.if(executionEnvironment ==“汽车”&& canusegpu）||executionEnvironment ==“ GPU”X = gpuArray(X);结尾％使用DLFEVAL评估模型梯度和损失% modelGradients function.[渐变，损失，状态] = dlfeval（@modelGradients，x，y，params）;params.state = state;％确定基于时间的衰减学习率计划的学习率。learnRate = initialLearnRate/(1 + decay*iteration);%更新网络参数s using the SGDM optimizer.[params.Learnables,velocity] = sgdmupdate(params.Learnables,gradients,velocity);％显示培训进度。if情节==“训练过程”D = duration(0,0,toc(start),'格式'，，，，'HH：MM：SS'）;addpoints(lineLossTrain,iteration,double(gather(extractdata(loss)))) title(“时代：”+ epoch +“，经过：”+ string(D)) drawnow结尾结尾结尾

    微调后计算网络的分类精度。

    cecurefacyFtraining = getNetworkAccuracy（Xtrain，Ytrain，params）;fprintf（'％.2F转移学习后的精度\ n'，准确训练）；
    1.00 accuracy after transfer learning

    Helper Functions

    本节提供了本示例中使用的助手功能的代码。

    这getNetworkCcuracyfunction evaluates the network performance by calculating the classification accuracy.

    functionaccuracy = getNetworkAccuracy(X,Y,onnxParams) N = size(X,4); Ypred = squeezenetFcn(X,onnxParams,'训练'，错误的）;[〜，yidx] = max（y，[]，1）;[〜，ypredidx] = max（ypred，[]，1）;numIncorrect = sum（abs（yidx-ypredidx）> 0）;精度= 1 -numIncorct/n;结尾

    这modelGradientsfunction calculates the loss and gradients.

    function[grad，损失，状态] = modelgradients（x，y，onnxparams）[y，state] = squeezenetfcn（x，onnxparams，'训练'，，，，true); loss = crossentropy(y,Y,'DataFormat'，，，，'CB'）;grad = dlgradient(loss,onnxParams.Learnables);结尾

    这Squeezenetonnxfunction generates an ONNX model of thesqueezenetnetwork.

    functionSqueezenetonnx() exportONNXNetwork(squeezenet,'squeezenet.onnx'）;结尾

    Input Arguments

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    网络参数，指定为ONNXParameters目的。params包含导入ONNX™模型的网络参数。

    要解冻的参数的名称，指定为'all'或字符串数​​组。通过设置解开所有不可检测的参数names至'all'。解冻knonlearnable parameters by defining the parameter names in the 1-by-k细绳arraynames。

    Example:[“ GPU_0_SL_PRED_B_0”，“ GPU_0_SL_PRED_W_0”]

    Data Types:char|细绳

    输出参数

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    网络参数，返回ONNXParameters目的。params包含由Unreezeparameters。

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