Train a Siamese Network for Dimensionality Reduction
This example shows how to train a Siamese network to compare handwritten digits using dimensionality reduction.
A Siamese network is a type of deep learning network that uses two or more identical subnetworks that have the same architecture and share the same parameters and weights. Siamese networks are typically used in tasks that involve finding the relationship between two comparable things. Some common applications for Siamese networks include facial recognition, signature verification , or paraphrase identification . Siamese networks perform well in these tasks because their shared weights mean there are fewer parameters to learn during training and they can produce good results with a relatively small amount of training data.
Siamese networks are particularly useful in cases where there are large numbers of classes with small numbers of observations of each. In such cases, there is not enough data to train a deep convolutional neural network to classify images into these classes. Instead, the Siamese network can determine if two images are in the same class. The network does this by reducing the dimensionality of the training data and using a distance-based cost function to differentiate between the classes.
This example uses a Siamese network for dimensionality reduction of a collection of images of handwritten digits. The Siamese architecture reduces the dimensionality by mapping images with the same class to nearby points in a low-dimensional space. The reduced-feature representation is then used to extract images from the dataset that are most similar to a test image. The training data in this example are images of size 28-by-28-by-1, giving an initial feature dimensionality of 784. The Siamese network reduces the dimensionality of the input images to two features and is trained to output similar reduced features for images with the same label.
You can also use Siamese networks to identify similar images by directly comparing them. For an example, see Train a Siamese Network to Compare Images.
Load and Preprocess Training Data
Load the training data, which consists of images of handwritten digits. The function
digitTrain4DArrayData loads the digit images and their labels.
[XTrain,TTrain] = digitTrain4DArrayData;
XTrain is a 28-by-28-by-1-by-5000 array containing 5000 single-channel images, each of size 28-by-28. The values of each pixel are between
TTrain is a categorical vector containing the labels for each observation, which are the numbers from 0 to 9 corresponding to the value of the written digit.
Display a random selection of the images.
perm = randperm(numel(TTrain),9); imshow(imtile(XTrain(:,:,:,perm),ThumbnailSize=[100 100]));
Create Pairs of Similar and Dissimilar Images
To train the network, the data must be grouped into pairs of images that are either similar or dissimilar. Here, similar images are defined as having the same label, while dissimilar images have different labels. The function
getSiameseBatch (defined in the Supporting Functions section of this example) creates randomized pairs of similar or dissimilar images,
pairImage2. The function also returns the label
pairLabel, which identifies if the pair of images is similar or dissimilar to each other. Similar pairs of images have
pairLabel = 1, while dissimilar pairs have
pairLabel = 0.
As an example, create a small representative set of five pairs of images
batchSize = 10; [pairImage1,pairImage2,pairLabel] = getSiameseBatch(XTrain,TTrain,batchSize);
Display the generated pairs of images.
figure tiledlayout("flow") for i = 1:batchSize nexttile imshow([pairImage1(:,:,:,i) pairImage2(:,:,:,i)]); if pairLabel(i) == 1 s = "similar"; else s = "dissimilar"; end title(s) end
In this example, a new batch of 180 paired images is created for each iteration of the training loop. This ensures that the network is trained on a large number of random pairs of images with approximately equal proportions of similar and dissimilar pairs.
Define Network Architecture
The Siamese network architecture is illustrated in the following diagram.
In this example, the two identical subnetworks are defined as a series of fully connected layers with ReLU layers. Create a network that accepts 28-by-28-by-1 images and outputs the two feature vectors used for the reduced feature representation. The network reduces the dimensionality of the input images to two, a value that is easier to plot and visualize than the initial dimensionality of 784.
For the first two fully connected layers, specify an output size of 1024 and use the He weight initializer.
For the final fully connected layer, specify an output size of two and use the He weights initializer.
layers = [ imageInputLayer([28 28],Normalization="none") fullyConnectedLayer(1024,WeightsInitializer="he") reluLayer fullyConnectedLayer(1024,WeightsInitializer="he") reluLayer fullyConnectedLayer(2,WeightsInitializer="he")];
To train the network with a custom training loop and enable automatic differentiation, convert the layer array to a
net = dlnetwork(layers);
Define Model Loss Function
Create the function
modelLoss (defined in the Supporting Functions section of this example). The
modelLoss function takes the Siamese
net and a mini-batch of input data
X2 with their labels
pairLabels. The function returns the loss values and the gradients of the loss with respect to the learnable parameters of the network.
The objective of the Siamese network is to output a feature vector for each image such that the feature vectors are similar for similar images, and notably different for dissimilar images. In this way, the network can discriminate between the two inputs.
Find the contrastive loss between the outputs from the last fully connected layer, the feature vectors
pairImage2, respectively. The contrastive loss for a pair is given by 
where is the value of the pair label ( for similar images; for dissimilar images), and is the Euclidean distance between two features vectors and : .
The parameter is used for constraint: if two images in a pair are dissimilar, then their distance should be at least , or a loss will be incurred.
The contrastive loss has two terms, but only one is ever non-zero for a given image pair. In the case of similar images, the first term can be non-zero and is minimized by reducing the distance between the image features and . In the case of dissimilar images, the second term can be non-zero, and is minimized by increasing the distance between the image features, to at least a distance of . The smaller the value of , the less constraining it is over how close a dissimilar pair can be before a loss is incurred.
Specify Training Options
Specify the value of to use during training.
margin = 0.3;
Specify the options to use during training. Train for 3000 iterations.
numIterations = 3000; miniBatchSize = 180;
Specify the options for Adam optimization:
Set the learning rate to
Initialize the trailing average gradient and trailing average gradient-square decay rates with
Set the gradient decay factor to
0.9and the squared gradient decay factor to
learningRate = 1e-4; trailingAvg = ; trailingAvgSq = ; gradDecay = 0.9; gradDecaySq = 0.99;
Train the model using a custom training loop.
TrainingProgressMonitor object. Because the timer starts when you create the monitor object, make sure that you create the object close to the training loop.
monitor = trainingProgressMonitor(Metrics="Loss",XLabel="Iteration");
Loop over the training data and update the network parameters at each iteration. For each iteration:
Extract a batch of image pairs and labels using the
getSiameseBatchfunction defined in the section Create Batches of Image Pairs.
Convert the image data to
dlarrayobjects with underlying type
singleand specify the dimension labels
"SSCB"(spatial, spatial, channel, batch).
Train on a GPU, if one is available. Using a GPU requires Parallel Computing Toolbox™ and a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox).
Evaluate the model loss and gradients using
Update the network parameters using the
Record the training loss in the training progress monitor.
iteration = 0; % Loop over mini-batches. while iteration < numIterations && ~monitor.Stop iteration = iteration + 1; % Extract mini-batch of image pairs and pair labels [X1,X2,pairLabels] = getSiameseBatch(XTrain,TTrain,miniBatchSize); % Convert mini-batch of data to dlarray. Specify the dimension labels % "SSCB" (spatial, spatial, channel, batch) for image data X1 = dlarray(single(X1),"SSCB"); X2 = dlarray(single(X2),"SSCB"); % If training on a GPU, then convert data to gpuArray. if canUseGPU X1 = gpuArray(X1); X2 = gpuArray(X2); end % Evaluate the model loss and gradients using dlfeval and the modelLoss % function listed at the end of the example. [loss,gradients] = dlfeval(@modelLoss,net,X1,X2,pairLabels,margin); % Update the Siamese network parameters. [net,trailingAvg,trailingAvgSq] = adamupdate(net,gradients, ... trailingAvg,trailingAvgSq,iteration,learningRate,gradDecay,gradDecaySq); % Update the training progress monitor. recordMetrics(monitor,iteration,Loss=loss); monitor.Progress = 100 * iteration/numIterations; end
Visualize Image Similarities
To evaluate how well the network is doing at dimensionality reduction, compute and plot the reduced features of a set of test data. Load the test data, which consists of images of handwritten digits similar to the training data. Convert the test data to
dlarray and specify the dimension labels
"SSCB" (spatial, spatial, channel, batch). If you are using a GPU, convert the test data to
[XTest,TTest] = digitTest4DArrayData; XTest = dlarray(single(XTest),"SSCB"); if canUseGPU XTest = gpuArray(XTest); end
Compute the reduced features of the test data.
FTest = predict(net,XTest);
For each group, plot the first two reduced features of the test data.
uniqueGroups = unique(TTest); colors = hsv(length(uniqueGroups)); figure hold on for k = 1:length(uniqueGroups) ind = TTest==uniqueGroups(k); plot(FTest(1,ind),FTest(2,ind),".",Color=colors(k,:)); end hold off xlabel("Feature 1") ylabel("Feature 2") title("2-D Feature Representation of Digits Images."); legend(uniqueGroups,Location="eastoutside");
Use the Trained Network to Find Similar Images
You can use the trained network to find a selection of images that are similar to each other out of a group. Extract a single test image from the test data and display it.
testIdx = randi(5000); testImg = XTest(:,:,:,testIdx); trialImgDisp = extractdata(testImg); figure imshow(trialImgDisp,InitialMagnification=500);
Create a group of images containing the test data but does not include the extracted test image.
groupX = XTest; groupX(:,:,:,testIdx) = ;
Find the reduced features of the test image using
trialF = predict(net,testImg);
Find the 2-D reduced feature representation of each of the images in the group using the trained network.
FGroupX = predict(net,groupX);
Use the reduced feature representation to find the nine images in the group that are closest to the test image, using the Euclidean distance metric. Display the images.
distances = vecnorm(extractdata(trialF - FGroupX)); [~,idx] = sort(distances); sortedImages = groupX(:,:,:,idx); sortedImages = extractdata(sortedImages); figure imshow(imtile(sortedImages(:,:,:,1:9)),InitialMagnification=500);
By reducing the images to a lower dimensionality, the network is able to identify images that are similar to the trial image. The reduced feature representation allows the network to discriminate between images that are similar and dissimilar. Siamese networks are often used in the context of facial or signature recognition. For example, you can train a Siamese network to accept an image of a face as an input, and return a set of the most similar faces from a database.
Model Loss Function
modelLoss takes the Siamese
net, a pair of mini-batch input data
X2, and the label
pairLabels. The function returns the contrastive loss between the reduced dimensionality features of the paired images and the gradients of the loss with respect to the learnable parameters in the network. Within this example, the function
modelLoss is introduced in the section Define Model Loss Function.
function [loss,gradients] = modelLoss(net,X1,X2,pairLabel,margin) % The modelLoss function calculates the contrastive loss between the % paired images and returns the loss and the gradients of the loss with % respect to the network learnable parameters % Pass first half of image pairs forward through the network F1 = forward(net,X1); % Pass second set of image pairs forward through the network F2 = forward(net,X2); % Calculate contrastive loss loss = contrastiveLoss(F1,F2,pairLabel,margin); % Calculate gradients of the loss with respect to the network learnable % parameters gradients = dlgradient(loss,net.Learnables); end function loss = contrastiveLoss(F1,F2,pairLabel,margin) % The contrastiveLoss function calculates the contrastive loss between % the reduced features of the paired images % Define small value to prevent taking square root of 0 delta = 1e-6; % Find Euclidean distance metric distances = sqrt(sum((F1 - F2).^2,1) + delta); % label(i) = 1 if features1(:,i) and features2(:,i) are features % for similar images, and 0 otherwise lossSimilar = pairLabel.*(distances.^2); lossDissimilar = (1 - pairLabel).*(max(margin - distances, 0).^2); loss = 0.5*sum(lossSimilar + lossDissimilar,"all"); end
Create Batches of Image Pairs
The following functions create randomized pairs of images that are similar or dissimilar, based on their labels. Within this example, the function
getSiameseBatch is introduced in the section Create Pairs of Similar and Dissimilar Images.
function [X1,X2,pairLabels] = getSiameseBatch(X,Y,miniBatchSize) % getSiameseBatch returns a randomly selected batch of paired images. % On average, this function produces a balanced set of similar and % dissimilar pairs. pairLabels = zeros(1, miniBatchSize); imgSize = size(X(:,:,:,1)); X1 = zeros([imgSize 1 miniBatchSize]); X2 = zeros([imgSize 1 miniBatchSize]); for i = 1:miniBatchSize choice = rand(1); if choice < 0.5 [pairIdx1, pairIdx2, pairLabels(i)] = getSimilarPair(Y); else [pairIdx1, pairIdx2, pairLabels(i)] = getDissimilarPair(Y); end X1(:,:,:,i) = X(:,:,:,pairIdx1); X2(:,:,:,i) = X(:,:,:,pairIdx2); end end function [pairIdx1,pairIdx2,pairLabel] = getSimilarPair(classLabel) % getSimilarPair returns a random pair of indices for images % that are in the same class and the similar pair label = 1. % Find all unique classes. classes = unique(classLabel); % Choose a class randomly which will be used to get a similar pair. classChoice = randi(numel(classes)); % Find the indices of all the observations from the chosen class. idxs = find(classLabel==classes(classChoice)); % Randomly choose two different images from the chosen class. pairIdxChoice = randperm(numel(idxs),2); pairIdx1 = idxs(pairIdxChoice(1)); pairIdx2 = idxs(pairIdxChoice(2)); pairLabel = 1; end function [pairIdx1,pairIdx2,pairLabel] = getDissimilarPair(classLabel) % getDissimilarPair returns a random pair of indices for images % that are in different classes and the dissimilar pair label = 0. % Find all unique classes. classes = unique(classLabel); % Choose two different classes randomly which will be used to get a dissimilar pair. classesChoice = randperm(numel(classes), 2); % Find the indices of all the observations from the first and second classes. idxs1 = find(classLabel==classes(classesChoice(1))); idxs2 = find(classLabel==classes(classesChoice(2))); % Randomly choose one image from each class. pairIdx1Choice = randi(numel(idxs1)); pairIdx2Choice = randi(numel(idxs2)); pairIdx1 = idxs1(pairIdx1Choice); pairIdx2 = idxs2(pairIdx2Choice); pairLabel = 0; end
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Wenpeg, Y., and H Schütze. "Convolutional Neural Network for Paraphrase Identification." In Proceedings of 2015 Conference of the North American Cahapter of the ACL, 2015, pp901-911. Available at Convolutional Neural Network for Paraphrase Identification on the ACL Anthology website.
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