MATLAB Examples

Transfer Learning Using AlexNet

This example shows how to fine-tune a pretrained AlexNet convolutional neural network to perform classification on a new collection of images.

Transfer learning is commonly used in deep learning applications. You can take a pretrained network and use it as a starting point to learn a new task. Fine-tuning a network with transfer learning is usually much faster and easier than training a network with randomly initialized weights from scratch. You can quickly transfer learned features to a new task using a smaller number of training images.

Contents

Load Data

Unzip and load the new images as an image datastore. imageDatastore automatically labels the images based on folder names and stores the data as an ImageDatastore object. An image datastore enables you to store large image data, including data that does not fit in memory, and efficiently read batches of images during training of a convolutional neural network.

unzip('MerchData.zip');
images = imageDatastore('MerchData',...
    'IncludeSubfolders',true,...
    'LabelSource','foldernames');

Divide the data into training and validation data sets. Use 70% of the images for training and 30% for validation. splitEachLabel splits the images datastore into two new datastores.

[trainingImages,validationImages] = splitEachLabel(images,0.7,'randomized');

This very small data set now contains 55 training images and 20 validation images. Display some sample images.

numTrainImages = numel(trainingImages.Labels);
idx = randperm(numTrainImages,16);
figure
for i = 1:16
    subplot(4,4,i)
    I = readimage(trainingImages,idx(i));
    imshow(I)
end

Load Pretrained Network

Load the pretrained AlexNet neural network. If Neural Network Toolbox™ Model for AlexNet Network is not installed, then the software provides a download link. AlexNet is trained on more than one million images and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the model has learned rich feature representations for a wide range of images.

net = alexnet;

Display the network architecture. The network has five convolutional layers and three fully connected layers.

net.Layers
ans = 

  25x1 Layer array with layers:

     1   'data'     Image Input                   227x227x3 images with 'zerocenter' normalization
     2   'conv1'    Convolution                   96 11x11x3 convolutions with stride [4  4] and padding [0  0  0  0]
     3   'relu1'    ReLU                          ReLU
     4   'norm1'    Cross Channel Normalization   cross channel normalization with 5 channels per element
     5   'pool1'    Max Pooling                   3x3 max pooling with stride [2  2] and padding [0  0  0  0]
     6   'conv2'    Convolution                   256 5x5x48 convolutions with stride [1  1] and padding [2  2  2  2]
     7   'relu2'    ReLU                          ReLU
     8   'norm2'    Cross Channel Normalization   cross channel normalization with 5 channels per element
     9   'pool2'    Max Pooling                   3x3 max pooling with stride [2  2] and padding [0  0  0  0]
    10   'conv3'    Convolution                   384 3x3x256 convolutions with stride [1  1] and padding [1  1  1  1]
    11   'relu3'    ReLU                          ReLU
    12   'conv4'    Convolution                   384 3x3x192 convolutions with stride [1  1] and padding [1  1  1  1]
    13   'relu4'    ReLU                          ReLU
    14   'conv5'    Convolution                   256 3x3x192 convolutions with stride [1  1] and padding [1  1  1  1]
    15   'relu5'    ReLU                          ReLU
    16   'pool5'    Max Pooling                   3x3 max pooling with stride [2  2] and padding [0  0  0  0]
    17   'fc6'      Fully Connected               4096 fully connected layer
    18   'relu6'    ReLU                          ReLU
    19   'drop6'    Dropout                       50% dropout
    20   'fc7'      Fully Connected               4096 fully connected layer
    21   'relu7'    ReLU                          ReLU
    22   'drop7'    Dropout                       50% dropout
    23   'fc8'      Fully Connected               1000 fully connected layer
    24   'prob'     Softmax                       softmax
    25   'output'   Classification Output         crossentropyex with 'tench', 'goldfish', and 998 other classes

Transfer Layers to New Network

The last three layers of the pretrained network net are configured for 1000 classes. These three layers must be fine-tuned for the new classification problem. Extract all layers, except the last three, from the pretrained network.

layersTransfer = net.Layers(1:end-3);

Transfer the layers to the new classification task by replacing the last three layers with a fully connected layer, a softmax layer, and a classification output layer. Specify the options of the new fully connected layer according to the new data. Set the fully connected layer to have the same size as the number of classes in the new data. To learn faster in the new layers than in the transferred layers, increase the WeightLearnRateFactor and BiasLearnRateFactor values of the fully connected layer.

numClasses = numel(categories(trainingImages.Labels))
layers = [
    layersTransfer
    fullyConnectedLayer(numClasses,'WeightLearnRateFactor',20,'BiasLearnRateFactor',20)
    softmaxLayer
    classificationLayer];
numClasses =

     5

If the training images differ in size from the image input layer, then you must resize or crop the image data. In this example, the images are the same size as the input size of AlexNet, so you do not need to resize or crop the images.

Train Network

Specify the training options. For transfer learning, keep the features from the early layers of the pretrained network (the transferred layer weights). Set InitialLearnRate to a small value to slow down learning in the transferred layers. In the previous step, you increased the learning rate factors for the fully connected layer to speed up learning in the new final layers. This combination of learning rate settings results in fast learning only in the new layers and slower learning in the other layers. When performing transfer learning, you do not need to train for as many epochs. An epoch is a full training cycle on the entire training data set. Specify the mini-batch size and validation data. The software validates the network every ValidationFrequency iterations during training, and automatically stops training if the validation loss stops improving. Validate the network once per epoch.

miniBatchSize = 10;
numIterationsPerEpoch = floor(numel(trainingImages.Labels)/miniBatchSize);
options = trainingOptions('sgdm',...
    'MiniBatchSize',miniBatchSize,...
    'MaxEpochs',4,...
    'InitialLearnRate',1e-4,...
    'Verbose',false,...
    'Plots','training-progress',...
    'ValidationData',validationImages,...
    'ValidationFrequency',numIterationsPerEpoch);

Train the network that consists of the transferred and new layers. By default, trainNetwork uses a GPU if one is available (requires Parallel Computing Toolbox™ and a CUDA-enabled GPU with compute capability 3.0 or higher). Otherwise, it uses a CPU. You can also specify the execution environment by using the 'ExecutionEnvironment' name-value pair argument of trainingOptions.

netTransfer = trainNetwork(trainingImages,layers,options);

Classify Validation Images

Classify the validation images using the fine-tuned network.

predictedLabels = classify(netTransfer,validationImages);

Display four sample validation images with their predicted labels.

idx = [1 5 10 15];
figure
for i = 1:numel(idx)
    subplot(2,2,i)
    I = readimage(validationImages,idx(i));
    label = predictedLabels(idx(i));
    imshow(I)
    title(char(label))
end

Calculate the classification accuracy on the validation set. Accuracy is the fraction of labels that the network predicts correctly.

valLabels = validationImages.Labels;
accuracy = mean(predictedLabels == valLabels)
accuracy =

     1

This trained network has high accuracy. If the accuracy is not high enough using transfer learning, then try feature extraction instead.