activations
Compute deep learning network layer activations
Syntax
Description
You can extract features using a trained deep learning network on either a CPU
or GPU. Using a GPU requires
Parallel
Computing Toolbox™ and a CUDA® enabled NVIDIA® GPU with compute capability 3.0 or higher. Specify the hardware requirements using the
ExecutionEnvironment name-value pair argument.
features = activations(___,Name,Value)'OutputAs','rows'
specifies the activation output format as 'rows'. Specify
name-value pair arguments after all other input arguments.
Examples
Feature Extraction Using AlexNet
This example shows how to extract learned image features from a pretrained convolutional neural network, and use those features to train an image classifier. Feature extraction is the easiest and fastest way to use the representational power of pretrained deep networks. For example, you can train a support vector machine (SVM) using fitcecoc (Statistics and Machine Learning Toolbox™) on the extracted features. Because feature extraction only requires a single pass through the data, it is a good starting point if you do not have a GPU to accelerate network training with.
Load Data
Unzip and load the sample 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 lets you store large image data, including data that does not fit in memory. Split the data into 70% training and 30% test data.
unzip('MerchData.zip'); imds = imageDatastore('MerchData', ... 'IncludeSubfolders',true, ... 'LabelSource','foldernames'); [imdsTrain,imdsTest] = splitEachLabel(imds,0.7,'randomized');
There are now 55 training images and 20 validation images in this very small data set. Display some sample images.
numImagesTrain = numel(imdsTrain.Labels); idx = randperm(numImagesTrain,16); for i = 1:16 I{i} = readimage(imdsTrain,idx(i)); end figure imshow(imtile(I))
Load Pretrained Network
Load a pretrained AlexNet network. If the Deep Learning Toolbox Model for AlexNet Network support package is not installed, then the software provides a download link. AlexNet is trained on more than a million images and can classify images into 1000 object categories. For example, 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' Grouped Convolution 2 groups of 128 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' Grouped Convolution 2 groups of 192 3x3x192 convolutions with stride [1 1] and padding [1 1 1 1]
13 'relu4' ReLU ReLU
14 'conv5' Grouped Convolution 2 groups of 128 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' and 999 other classes
The first layer, the image input layer, requires input images of size 227-by-227-by-3, where 3 is the number of color channels.
inputSize = net.Layers(1).InputSize
inputSize = 1×3
227 227 3
Extract Image Features
The network constructs a hierarchical representation of input images. Deeper layers contain higher-level features, constructed using the lower-level features of earlier layers. To get the feature representations of the training and test images, use activations on the fully connected layer 'fc7'. To get a lower-level representation of the images, use an earlier layer in the network.
The network requires input images of size 227-by-227-by-3, but the images in the image datastores have different sizes. To automatically resize the training and test images before they are input to the network, create augmented image datastores, specify the desired image size, and use these datastores as input arguments to activations.
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain); augimdsTest = augmentedImageDatastore(inputSize(1:2),imdsTest); layer = 'fc7'; featuresTrain = activations(net,augimdsTrain,layer,'OutputAs','rows'); featuresTest = activations(net,augimdsTest,layer,'OutputAs','rows');
Extract the class labels from the training and test data.
YTrain = imdsTrain.Labels; YTest = imdsTest.Labels;
Fit Image Classifier
Use the features extracted from the training images as predictor variables and fit a multiclass support vector machine (SVM) using fitcecoc (Statistics and Machine Learning Toolbox).
mdl = fitcecoc(featuresTrain,YTrain);
Classify Test Images
Classify the test images using the trained SVM model and the features extracted from the test images.
YPred = predict(mdl,featuresTest);
Display four sample test images with their predicted labels.
idx = [1 5 10 15]; figure for i = 1:numel(idx) subplot(2,2,i) I = readimage(imdsTest,idx(i)); label = YPred(idx(i)); imshow(I) title(label) end
Calculate the classification accuracy on the test set. Accuracy is the fraction of labels that the network predicts correctly.
accuracy = mean(YPred == YTest)
accuracy = 1
This SVM has high accuracy. If the accuracy is not high enough using feature extraction, then try transfer learning instead.
Input Arguments
Output Arguments
Algorithms
All functions for deep learning training, prediction, and validation in
Deep Learning
Toolbox™ perform computations using single-precision, floating-point arithmetic.
Functions for deep learning include trainNetwork, predict,
classify, and
activations.
The software uses single-precision arithmetic when you train networks using both CPUs and
GPUs.
References
[1] M. Kudo, J. Toyama, and M. Shimbo. "Multidimensional Curve Classification Using Passing-Through Regions." Pattern Recognition Letters. Vol. 20, No. 11–13, pages 1103–1111.
[2] UCI Machine Learning Repository: Japanese Vowels Dataset. https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels