1. Overview of algorithm theory

The MNIST handwritten digit database is a commonly used data set in machine learning, which contains handwritten pictures of 10 numbers from 0 to 9. This paper introduces a MNIST handwritten digit recognition algorithm based on the AutoEncoder autoencoder. By training the autoencoder to perform feature extraction and dimensionality reduction on the MNIST dataset, the extracted features are classified and recognized. The algorithm performs well on the MNIST dataset and has a high recognition accuracy.
The main steps of the algorithm are as follows:

Step 1: Data preprocessing
Load handwritten digital pictures from the MNIST database, preprocess the pictures, and scale the pixel values to the range [0, 1] to facilitate the training of the neural network.

Step 2: Build AutoEncoder Autoencoder Autoencoder
is a kind of unsupervised learning neural network, which is used to reconstruct the output similar to the original input after the input data is encoded and decoded. In this algorithm, a multi-layer autoencoder network is constructed, including an input layer, an encoding layer and a decoding layer. The number of neurons in the encoding layer is small, so as to realize the dimensionality reduction of the input data. Specific steps are as follows:

a) Input layer: Flatten the MNIST handwritten digital image into a one-dimensional vector as the input of the autoencoder.
b) Encoding layer: select the appropriate number of neurons, encode the input features, and obtain the encoded feature vector.
c) Decoding layer: optimize the network parameters through the backpropagation algorithm, realize the decoding of the encoded features, and obtain the reconstructed output.
d) Loss function: Define an appropriate loss function to measure the difference between the reconstructed output and the original input, and optimize the network parameters by minimizing the loss function.
The forward propagation process of the autoencoder

The loss function of the autoencoder, the reconstruction error loss function:

The MNIST handwritten digit database recognition algorithm based on AutoEncoder is an effective image classification algorithm. Through the feature extraction and dimension reduction of the autoencoder, a lower-dimensional feature representation can be obtained, and a higher recognition accuracy can be achieved on the MNIST data set. The algorithm can also be extended to other image recognition tasks, and has good versatility and applicability. In practical applications, the parameters of the autoencoder and SVM can be tuned according to the specific situation to further improve the recognition performance and efficiency.

2. Some core programs

.....................................................

%训练第一个自动编码器（Autoencoder）
hiddenSize1 = 100;
autoenc1    = trainAutoencoder(xTrainImages,hiddenSize1,'MaxEpochs',500,'L2WeightRegularization',0.004,'SparsityRegularization',4,'SparsityProportion',0.15,'ScaleData',false);
figure
plotWeights(autoenc1);
 
view(autoenc1)

%获取第一个自动编码器的特征
feat1 = encode(autoenc1,xTrainImages);
view(softnet)

% 将自动编码器和softmax分类层堆叠成深度神经网络（Deep Neural Network，DNN）
deepnet = stack(autoenc1,autoenc2,softnet);
 
view(deepnet)


%进行识别
tmp2s = imgs(:,:,1);
y = deepnet(tmp2s(:));
y
[V,I] = max(y);
disp('识别结果为：');
I
0025

3. Algorithm running software version

MATLAB2022a

4. Algorithm operation rendering preview

5. Algorithm complete program engineering

OOOOO

OOO

Matlab simulation of MNIST handwritten digit database recognition based on AutoEncoder

1. Overview of algorithm theory

2. Some core programs

3. Algorithm running software version

4. Algorithm operation rendering preview

5. Algorithm complete program engineering

Guess you like