基于OCR的LSTM-CNN深度学习网络数字图片识别算法matlab仿真.zip

cifar10Data = tempdir; url = 'https://www.cs.toronto.edu/~kriz/cifar-10-matlab.tar.gz'; helperCIFAR10Data.download(url,cifar10Data); [trainingImages,trainingLabels,testImages,testLabels] = helperCIFAR10Data.load('cifar10Data'); size(trainingImages) numImageCategories = 10; categories(trainingLabels) % Create the image input layer for 32x32x3 CIFAR-10 images [height, width, numChannels, ~] = size(trainingImages); imageSize = [height width numChannels]; inputLayer = imageInputLayer(imageSize); % Convolutional layer parameters filter size filterSize = [5 5]; numFilters = 32; middleLayers = [ % The first convolutional layer has a bank of 32 5x5x3 filters. A % symmetric padding of 2 pixels is added to ensure that image borders % are included in the processing. This is important to avoid % information at the borders being washed away too early in the % network. convolution2dLayer(filterSize, numFilters, 'Padding', 2) %(n+2p-f)/s+1 % Note that the third dimension of the filter can be omitted because it % is automatically deduced based on the connectivity of the network. In % this case because this layer follows the image layer, the third % dimension must be 3 to match the number of channels in the input % image. % Next add the ReLU layer: reluLayer() % Follow it with a max pooling layer that has a 3x3 spatial pooling area % and a stride of 2 pixels. This down-samples the data dimensions from % 32x32 to 15x15. maxPooling2dLayer(3, 'Stride', 2) % Repeat the 3 core layers to complete the middle of the network. convolution2dLayer(filterSize, numFilters, 'Padding', 2) reluLayer() maxPooling2dLayer(3, 'Stride',2) convolution2dLayer(filterSize, 2 * numFilters, 'Padding', 2) reluLayer() maxPooling2dLayer(3, 'Stride',2) ]; finalLayers = [ % Add a fully connected layer with 64 output neurons. The output size of % this layer will be an array with a length of 64. fullyConnectedLayer(64) % Add an ReLU non-linearity. reluLayer % Add the last fully connected layer. At this point, the network must % produce 10 signals that can be used to measure whether the input image % belongs to one category or another. This measurement is made using the % subsequent loss layers. fullyConnectedLayer(numImageCategories) % Add the softmax loss layer and classification layer. The final layers use % the output of the fully connected layer to compute the categorical % probability distribution over the image classes. During the training % process, all the network weights are tuned to minimize the loss over this % categorical distribution. softmaxLayer classificationLayer ]; layers = [ inputLayer middleLayers finalLayers ]; layers(2).Weights = 0.0001 * randn([filterSize numChannels numFilters]); % Set the network training options opts = trainingOptions('sgdm', ... 'Momentum', 0.9, ... 'InitialLearnRate', 0.001, ... 'LearnRateSchedule', 'piecewise', ... 'LearnRateDropFactor', 0.1, ... 'LearnRateDropPeriod', 8, ... 'L2Regularization', 0.004, ... 'MaxEpochs', 40, ... 'MiniBatchSize', 128, ... 'Verbose', true); % A trained network is loaded from disk to save time when running the % example. Set this flag to true to train the network. doTraining = false; if doTraining % Train a network. cifar10Net = trainNetwork(trainingImages, trainingLabels, layers, opts); else % Load pre-trained detector for the example. load('rcnnStopSigns.mat','cifar10Net') end % Extract the first convolutional layer weights w = cifar10Net.Layers(2).Weights; % rescale the weights to the range [0, 1] for better visualization w = rescale(w); figure montage(w) % Run the network on the test set. YTest = classify(cifar10Net, testImages); % Calculate the accuracy. accuracy = sum(YTest == testLabels)/numel(testLabels)