我正在运行一个从网络研讨会获得的示例。这是代码：
%% Fine Tuning A Deep Neural Network
clear; clc;close all;
imagenet_cnn = load('imagenet-cnn');
net = imagenet_cnn.convnet;
net.Layers
%% Perform net surgery
layers = net.Layers(1:end-3);
layers(end+1) = fullyConnectedLayer(12, 'Name', 'fc8_2')
layers(end+1) = softmaxLayer('Name','prob_2');
layers(end+1) = classificationLayer('Name','classificationLayer_2')
%% Setup learning rates for fine-tuning
% fc 8 - bump up learning rate for last layers
layers(end-2).WeightLearnRateFactor = 100;
layers(end-2).WeightL2Factor = 1;
layers(end-2).BiasLearnRateFactor = 20;
layers(end-2).BiasL2Factor = 0;
%% Load Image Data
rootFolder = fullfile('E:\Universidad\Tesis\Matlab', 'TesisDataBase');
categories = {'Avion','Banana','Carro','Gato', 'Mango','Perro','Sandia','Tijeras','Silla','Mouse','Calculadora','Arbol'};
imds = imageDatastore(fullfile(rootFolder, categories), 'LabelSource', 'foldernames');
tbl = countEachLabel(imds);
%% Equalize number of images of each class in training set
minSetCount = min(tbl{:,2}); % determine the smallest amount of images in a category
% Use splitEachLabel method to trim the set.
imds = splitEachLabel(imds, minSetCount);
% Notice that each set now has exactly the same number of images.
countEachLabel(imds)
[trainingDS, testDS] = splitEachLabel(imds, 0.7,'randomize');
% Convert labels to categoricals
trainingDS.Labels = categorical(trainingDS.Labels);
trainingDS.ReadFcn = @readFunctionTrain;
%% Setup test data for validation
testDS.Labels = categorical(testDS.Labels);
testDS.ReadFcn = @readFunctionValidation;
%% Fine-tune the Network
miniBatchSize = 32; % lower this if your GPU runs out of memory.
numImages = numel(trainingDS.Files);
numIterationsPerEpoch = 250;
maxEpochs = 62;
lr = 0.01;
opts = trainingOptions('sgdm', ...
'InitialLearnRate', lr,...
'LearnRateSchedule', 'none',...
'L2Regularization', 0.0005, ...
'MaxEpochs', maxEpochs, ...
'MiniBatchSize', miniBatchSize);
net = trainNetwork(trainingDS, layers, opts);
如您所见，此代码使用众所周知的 AlexNet 作为第一个开始，然后删除最后 3 层，以便放置 3 个具有新任务所需神经元数量的新层。
测试和训练的读取函数是相同的，你有其中之一：
function Iout = readFunctionTrain(filename)
% Resize the flowers images to the size required by the network.
I = imread(filename);
% Some images may be grayscale. Replicate the image 3 times to
% create an RGB image.
if ismatrix(I)
I = cat(3,I,I,I);
end
% Resize the image as required for the CNN.
Iout = imresize(I, [227 227]);
该代码在网络研讨会上运行良好，他们使用它对通过 matworks 门的汽车和潜艇进行分类。
问题是当我用自己的图像尝试新网络时，新网络没有学习，我有一个包含 12 个类别的数据集，每个类别大约有 1000 张图像，所有这些图像都是从 ImageNET 下载的。
网络并没有提高它的 Mini batch 准确率，实际上有时它会提高，但速度很慢。
我也做了这个页面的教程 Matlab Deep Learning ToolBox
它适用于我的图像。所以，我不明白我的微调有什么问题。谢谢。