Table of contents
1. Preview of algorithm operation renderings
2.Algorithm running software version
4. Overview of algorithm theory
5. Algorithm complete program engineering
1. Preview of algorithm operation renderings
2.Algorithm running software version
matlab2022a
3. Some core programs
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% 获取输入层的尺寸
Input_Layer_Size = net.Layers(1).InputSize(1:2);
% 调整训练、验证和测试数据集的图像尺寸
Resized_Training_Dataset = augmentedImageDatastore(Input_Layer_Size ,Training_Dataset);
Resized_Validation_Dataset = augmentedImageDatastore(Input_Layer_Size ,Validation_Dataset);
Resized_Testing_Dataset = augmentedImageDatastore(Input_Layer_Size ,Testing_Dataset);
% 设置训练参数
maxEpochs = 20;
Minibatch_Size = 8;
Validation_Frequency = floor(numel(Resized_Training_Dataset.Files)/Minibatch_Size);
Training_Options = trainingOptions('sgdm', ...
'MiniBatchSize', Minibatch_Size, ...
'MaxEpochs', maxEpochs, ...
'InitialLearnRate', 1e-3, ...
'Shuffle', 'every-epoch', ...
'ValidationData', Resized_Validation_Dataset, ...
'ValidationFrequency', Validation_Frequency, ...
'Verbose', false, ...
'Plots', 'training-progress');
% 使用训练数据训练新网络
net = trainNetwork(Resized_Training_Dataset, New_Network, Training_Options);
save gnet.mat
604. Overview of algorithm theory
Animal species recognition algorithms are based on deep learning technology, especially convolutional neural networks (CNN), such as GoogleNet. The main principle of this algorithm is to predict the type of animal by learning and identifying features in images.
GoogleNet, also known as Inception v1, is a deep learning model developed by Google in 2014. GoogleNet is characterized by a large depth, which increases the complexity of the network, and introduces the "Inception module", which allows the network to process convolution kernels of different sizes in the same layer, thereby capturing features of different scales of the image.
The animal species recognition algorithm based on GoogleNet mainly includes the following steps:
- Data preprocessing: First, we need to preprocess the image, including resizing, normalizing pixel values, etc.
- Build the GoogleNet model: Next, we need to build the GoogleNet model. The GoogleNet model consists of multiple Inception modules and other layers.
- Training the model: We then train the model using a dataset of annotated animal images. In this process, the model will learn how to recognize various characteristics of animals.
- Test the model: Finally, we test the performance of the model with some images that have not appeared in the training set.
As for mathematical formulas, the main operations of convolutional neural networks include convolution, pooling, activation function, etc. The formulas involved here are complicated, so I'll try to simplify them as much as possible:
- Convolution: Suppose we have an input image X and a convolution kernel K, then the convolution operation can be expressed by the following formula:
s(t) = (X * K)(t) = ∫X(a)K(t - a)da
Among them, * represents the convolution operation, and t is a two-dimensional coordinate.
- Pooling: Pooling operations generally use Max Pooling or Average Pooling. Taking max pooling as an example, assuming we have a 2x2 pooling window, then the max pooling result is the maximum value in this window.
- Activation function: There are many types of activation functions, such as ReLU (Rectified Linear Unit), Sigmoid, etc. The functional form of ReLU can be expressed as:
f(x) = max(0, x)
This is just part of the math behind convolutional neural networks. In fact, deep learning involves a wide range of mathematics and computer science knowledge, including linear algebra, calculus, probability theory, optimization theory, etc.
The above are the basic principles and mathematical formulas of the animal species recognition algorithm based on GoogleNet. As research and practice in this area are still evolving, more advanced models and algorithms may be developed.
5. Algorithm complete program engineering
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