m Matlab simulation of pet dog type recognition system based on deep learning network, with GUI interface

Table of contents

2. Summary of theoretical knowledge involved in algorithms

1. Algorithm simulation effect

The matlab2022a simulation results are as follows:

2. Summary of theoretical knowledge involved in algorithms

The pet dog type recognition system based on deep learning network is a method of image classification using deep learning technology, which can automatically learn the features in the image and classify the images based on these features. The principles and mathematical formulas of the system are as follows:

Deep neural network model: In the pet dog species recognition system, a deep neural network model is used to learn features in images. The model consists of multiple neurons, each of which receives input data and outputs a numerical value. Through the combination of multiple neurons, various features in the image can be extracted.
Feature extraction: Deep neural network models can automatically extract features from raw image data. This process is implemented through multiple convolutional and pooling layers. The convolutional layer can detect various features in the image, such as color, texture, etc., while the pooling layer can aggregate these features to reduce computational complexity.
Classifier: The pet dog species recognition system uses a classifier to classify the extracted features. The classifier can be a softmax classifier, a support vector machine (SVM), etc. By training a deep neural network model and classifier, the system can accurately classify pet dog species.
Loss function: In order to optimize deep neural network models and classifiers, a loss function needs to be defined. This loss function can represent the accuracy and generalization ability of pet dog species recognition. During the training process, the model and classifier are optimized by minimizing this loss function to improve the performance of the pet dog species recognition system.
Backpropagation algorithm: When training deep neural network models and classifiers, the backpropagation algorithm is used to update the weight and bias values of each neuron. The algorithm automatically calculates gradients and updates weights and bias values to minimize the loss function.
The main steps of the pet dog type recognition system based on deep learning network are as follows:

Data preprocessing: Preprocess the collected pet dog image data, such as adjusting image size, normalizing pixel values, etc.
Build a deep neural network model: Build a deep neural network model, including multiple convolutional layers, pooling layers and fully connected layers.
Train the model: Train a deep neural network model and classifier using a dataset of pet dog images. Optimize models and classifiers by minimizing the loss function.
Test model: Use the test set to test the trained model to evaluate the accuracy and generalization ability of the pet dog species recognition system.
Apply the model: Apply the trained model to actual scenarios, such as as a pet dog breed identification tool, allowing users to upload images and immediately obtain prediction results of pet dog breeds.
The pet dog type recognition system based on deep learning network has the following advantages:

High degree of automation: The system can automatically learn features from original image data and automatically classify images, reducing the degree of manual intervention.
High accuracy and generalization ability: By training deep neural network models and classifiers, the system can have high accuracy and generalization ability, and can be adapted to various image classification tasks.
Scalability: The pet dog species identification system based on deep learning network can be continuously expanded and improved with the increase of data sets and the development of technology.
In general, the pet dog category recognition system based on deep learning network is a method of image classification using deep learning technology. It has the advantages of high degree of automation, high accuracy, strong generalization ability, and good scalability. It can provide better solutions for actual application scenarios and provide better solutions for actual application scenarios.

3.MATLAB core program

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% 设置训练选项
maxEpochs = NEpochs;
Minibatch_Size = NMB;
Validation_Frequency = floor(numel(Resized_Training_Dataset.Files)/Minibatch_Size);
Training_Options = trainingOptions('sgdm', ...
    'MiniBatchSize', Minibatch_Size, ...
    'MaxEpochs', maxEpochs, ...
    'InitialLearnRate', LR, ...
    'Shuffle', 'every-epoch', ...
    'ValidationData', Resized_Validation_Dataset, ...
    'ValidationFrequency', Validation_Frequency, ...
    'Verbose', false, ...
    'Plots', 'training-progress');

% 使用训练选项训练网络
net = trainNetwork(Resized_Training_Dataset, New_Network, Training_Options);
% 保存训练后的网络
save gnet.mat net




function edit7_Callback(hObject, eventdata, handles)
% hObject    handle to edit7 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of edit7 as text
%        str2double(get(hObject,'String')) returns contents of edit7 as a double


% --- Executes during object creation, after setting all properties.
function edit7_CreateFcn(hObject, eventdata, handles)
% hObject    handle to edit7 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function edit8_Callback(hObject, eventdata, handles)
% hObject    handle to edit8 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of edit8 as text
%        str2double(get(hObject,'String')) returns contents of edit8 as a double


% --- Executes during object creation, after setting all properties.
function edit8_CreateFcn(hObject, eventdata, handles)
% hObject    handle to edit8 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end

function edit9_Callback(hObject, eventdata, handles)
% hObject    handle to edit9 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of edit9 as text
%        str2double(get(hObject,'String')) returns contents of edit9 as a double


function edit10_Callback(hObject, eventdata, handles)
% hObject    handle to edit10 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of edit10 as text
%        str2double(get(hObject,'String')) returns contents of edit10 as a double
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