MATLAB深度学习工具箱使用教程

一、introduction

深度学习对于图像识别

二、using pretrained Networks

1、加载并显示图像

img1 = imread('file01.jpg');

imshow(img1)

2、预测

deepnet = alexnet; %获取预训练模型

pred1 = classify(deepnet, img1); %预测img1

3、获取其他预训练模型




4、examine network layers


deepnet = alexnet;  %获取预训练网络

ly = deepnet.Layers;%获取网络layers

inlayer = ly(1); %获取输入层结构

insz = inlayer.InputSize; %获取输入层size

outlayer = ly(end); %获取输出层

categorynames = outlayer.Classes; %获取最后一层的class

5、investigating predictions

分类函数返回输入图像的预测类,但是有办法知道网络对这个分类有多“自信”吗?在决定如何处理输出时,考虑这种信心可能很重要。

为了将输入分类为n个类中的一个,神经网络有一个由n个神经元组成的输出层,每个神经元对应一个类。通过网络传递输入结果是为每个神经元计算一个数值。这些数值表示网络对属于每个类的输入概率的预测。


img = imread('file01.jpg');

imshow(img)

net = alexnet;

categorynames = net.Layers(end).ClassNames;

[pred, scores] = classify(net, img);  %获得预测结果和自信分数

bar(scores); %Display scores

highscores = scores > 0.01; %Threshold scores

bar(scores(highscores)); %Display thresholded scores

xticklabels(categorynames(highscores)); %Add tick labels

三、managing collections of data

1、creating a datastore

ls *.jpg

net = alexnet;

imds = imageDatastore('file*.jpg'); %创建datastore

fname = imds.Files; %提取文件名

img = readimage(imds, 7);  %读取图像

preds = classify(net, imds); %图片分类


2、 Preparing Images to Use as Input: Adjust input images

Process Images for Classification

img = imread('file01.jpg');

imshow(img);

sz = size(img);  %读取图像大小

net = alexnet;

insz = net.Layers(1).InputSize;  %输入层图像大小

img = imresize(img, [227, 227]);  

imshow(img);

3、Processing Images in a Datastore: (2/3) Creating an augmented image datastore

Resize Images in a Datastore

ls *.jpg

net = alexnet;

imds = imageDatastore('*.jpg');

auds = augmentedImageDatastore([227,227], imds); %Create augmentedImageDatastore

preds = classify(net, auds)

Processing Images in a Datastore: (3/3) Color preprocessing with augmented image datastores

augmentedImageDatastore可以对彩色图片进行处理

ls *.jpg

net = alexnet;

imds = imageDatastore('file*.jpg');

montage(imds); %Display images in imds

auds = augmentedImageDatastore([227,227], imds, 'ColorPreprocessing', 'gray2rgb') %Create augmentedImageDatastore

preds = classify(net, auds)

Create a Datastore Using Subfolders

net = alexnet;

flwrds = imageDatastore('Flowers', 'IncludeSubfolders',true);

preds = classify(net,flwrds)

四、transfer learn

1、原因

(1)原有NET不能解决有效自己的问题

(2)自己训练一个全新的网络--网络结构与随机权重,需要具有网络架构方面的知识和经验、大量的训练数据、大量的计算时间

2、Components Needed for Transfer Learning: (1/2) The components of transfer learning


3、 Preparing Training Data: (1/3) Labeling images

Label Images in a Datastore

load pathToImages

flwrds = imageDatastore(pathToImages,'IncludeSubfolders',true);  %This code creates a datastore of 960 flower images.

flowernames = flwrds.Labels

flwrds = imageDatastore(pathToImages,'IncludeSubfolders',true,'LabelSource','foldernames')  %Create datastore with labels

flowernames = flwrds.Labels  %Extract new labels

Preparing Training Data: (2/3) Split data for training and testing

Split Data for Training and Testing

Instructions are in the task pane to the left. Complete and submit each task one at a time.

This code creates a datastore of 960 flower images.

load pathToImages

flwrds = imageDatastore(pathToImages,'IncludeSubfolders',true,'LabelSource','foldernames')

Task 1

Split datastore

[flwrTrain, flwrTest] = splitEachLabel(flwrds, 0.6)

Task 2

Split datastore randomly

[flwrTrain, flwrTest] = splitEachLabel(flwrds, 0.8, 'randomized')

Task 3

Split datastore by number of images

[flwrTrain, flwrTest] = splitEachLabel(flwrds,50)


Preparing Training Data: (3/3) Augmented training data


4、微调思路
(1)Recall that a feed-forward network is represented in MATLAB as an array of layers. This makes it easy to index into the layers of a network and change them.

(2)To modify a preexisting network, you create a new layer

(3)then index into the layer array that represents the network and overwrite the chosen layer with the newly created layer.

(4)As with any indexed assignment in MATLAB, you can combine these steps into one line.

Modifying Network Layers: (2/2) Modify layers of a pretrained network

Modify Network Layers

Instructions are in the task pane to the left. Complete and submit each task one at a time.

This code imports AlexNet and extracts its layers.

anet = alexnet;

layers = anet.Layers

Task 1

Create new layer

fc = fullyConnectedLayer(12)

Task 2

Replace 23rd layer

layers(23) = fc

Task 3

Replace last layer

layers(end) = classificationLayer

Setting Training Options

Set Training Options

Instructions are in the task pane to the left. Complete and submit each task one at a time.

Task 1

Set default options

opts = trainingOptions('sgdm');

Task 2

Set initial learning rate

opts = trainingOptions('sgdm','InitialLearnRate',0.001);

Training the Network: (4/4) Summary example

Transfer Learning Example Script

The code below implements transfer learning for the flower species example in this chapter. It is available as the script trainflowers.mlx in the course example files. You can download the course example files from the help menu in the top-right corner. You can find more information on this dataset at the 17 Category Flower Dataset page from the University of Oxford. 

Note that this example can take some time to run if you run it on a computer that does not have a supported GPU.

Get training images

flower_ds = imageDatastore('Flowers','IncludeSubfolders',true,'LabelSource','foldernames');[trainImgs,testImgs] = splitEachLabel(flower_ds,0.6);numClasses = numel(categories(flower_ds.Labels));


Create a network by modifying AlexNet

net = alexnet;layers = net.Layers;layers(end-2) = fullyConnectedLayer(numClasses);layers(end) = classificationLayer;


Set training algorithm options

options = trainingOptions('sgdm','InitialLearnRate', 0.001);


Perform training

[flowernet,info] = trainNetwork(trainImgs, layers, options);


Use trained network to classify test images

testpreds = classify(flowernet,testImgs);

4.7 Evaluating Performance: (1/3) Evaluating training and test performance

Evaluate Performance

Instructions are in the task pane to the left. Complete and submit each task one at a time.

This code loads the training information of flowernet.

load pathToImages

load trainedFlowerNetwork flowernet info

Task 1

Plot training loss

plot(info.TrainingLoss)

This code creates a datastore of the flower images.

dsflowers = imageDatastore(pathToImages,'IncludeSubfolders',true,'LabelSource','foldernames');

[trainImgs,testImgs] = splitEachLabel(dsflowers,0.98);

Task 2

Classify images

flwrPreds = classify(flowernet,testImgs)

Evaluating Performance: (2/3) Investigating test performance

Investigate test performance

Instructions are in the task pane to the left. Complete and submit each task one at a time.

This code sets up the Workspace for this activity.

load pathToImages.mat

pathToImages

flwrds = imageDatastore(pathToImages,'IncludeSubfolders',true,'LabelSource','foldernames');

[trainImgs,testImgs] = splitEachLabel(flwrds,0.98);

load trainedFlowerNetwork flwrPreds

Task 1

Extract labels

flwrActual = testImgs.Labels

Task 2

Count correct

numCorrect = nnz(flwrPreds == flwrActual)

Task 3

Calculate fraction correct

fracCorrect = numCorrect/numel(flwrPreds)

Task 4

Display confusion matrix

confusionchart(testImgs.Labels,flwrPreds)

Evaluating Performance: (3/3) Improving performance

MATLAB Course

Transfer Learning Summary

Transfer Learning Function Summary

Create a network

FunctionDescription

alexnetLoad pretrained network “AlexNet”

supported networksView list of available pretrained networks

fullyConnectedLayerCreate new fully connected network layer

classificationLayerCreate new output layer for a classification network


Get training images

FunctionDescription

imageDatastoreCreate datastore reference to image files

augmentedImageDatastorePreprocess a collection of image files

splitEachLabelDivide datastore into multiple datastores


Set training algorithm options

FunctionDescription

trainingOptionsCreate variable containing training algorithm options


Perform training

FunctionDescription

trainNetworkPerform training


Use trained network to perform classifications

FunctionDescription

classifyObtain trained network's classifications of input images


Evaluate trained network

FunctionDescription

nnzCount non-zero elements in an array

confusionchartCalculate confusion matrix

heatmapVisualize confusion matrix as a heatmap

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