在上一篇部落格中，講解了怎麼用matlab搭建CNN網絡模型，并給出了訓練過程與結果。但是結果不是很滿意，大概訓練精度在80%左右，現在給出改進方案。
1.首先，我們可以把CNN濾波輸出數改大點，從原來的32改為numFilters = 128，完整的程式如下：
[trainingImages,trainingLabels,testImages,testLabels] = helperCIFAR10Data.load('cifar10Data');
numImageCategories = 10;
categories(trainingLabels)
[height, width, numChannels, ~] = size(trainingImages);
imageSize = [height width numChannels];
inputLayer = imageInputLayer(imageSize)
% Convolutional layer parameters
filterSize = [5 5];
numFilters = 128;
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)
% 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(256)
% 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,...
'Plots','training-progress');
% A trained network is loaded from disk to save time when running the
% example. Set this flag to true to train the network.
doTraining = true;
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)
訓練結果如下：
從圖中可以看出結果非常好，訓練結果達到96%以上，然而測試結果基本沒有提高，
現在這種情況屬于High variance,這是由于過拟合造成的，現在要去解決這個問題。