手把手教你用matlab做深度学习(二)- --CNN

在上一篇博客中，讲解了怎么用matlab搭建CNN网络模型，并给出了训练过程与结果。但是结果不是很满意，大概训练精度在80%左右，现在给出改进方案。

1.首先，我们可以把CNN滤波输出数改大点，从原来的32改为numFilters = 128，完整的程序如下：

[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