Deep learning is a type of supervised machine learning in which a model learns to perform classification tasks directly from images, text, or sound.
Deep learning is usually implemented using a neural network.
The term “deep” refers to the number of layers in the network—the more layers, the deeper the network.
Link to download dataset https://www.kaggle.com/shawon10/ckplus
How CNNs Work
- A convolutional neural network can have hundreds of layers and each layer learn to detect different features of an image.
- Filters are applied to each training image at different resolutions and size, and the output of each convolved image is used as the input to the next layer.
- The filters can start as very simple features, such as brightness and edges, and later on it goes deep to extract complex features.
- Like other neural networks, a CNN is composed of an input layer, an output layer, and many hidden layers in between.
There are 7 steps as in below figure. For explanation click here
Create Image Datstore
Save all images in a single folder and create sub folders for different set of samples.
imds = imageDatastore('gesture', ... 'IncludeSubfolders',true,'LabelSource','foldernames');
Split Data for Training and validation
[imdstrain, imdsvalid]=splitEachLabel(imds,.8,'randomize'); CountLabel = imds.countEachLabel aa=read(imds); size(aa)
Define the Network Layers
Image Input Layer An ImageInputLayer is where you specify the image size
Convolutional Layer It is a CNN filter , where inputs are filter size and number of neurons.
Batch Normalization Layer Batch normalization layers normalize the activations and gradients propagating through a network.
ReLU Layer It is a linear rectified unit , it is used to convert negative feature to 0.
Max-Pooling Layer It is used for down sampling and to reduce redundant features.
Fully Connected Layer It is used to connect all neurons and we provide number of classes in it.
Softmax Layer It is used to find out the probability of object in the image.
Classification Layer Based on the softmax layer it identify the object from the image.
Define the convolutional neural network architecture.
layers = [ imageInputLayer([28 28 1]) convolution2dLayer(3,16,'Padding',1) batchNormalizationLayer reluLayer maxPooling2dLayer(2,'Stride',2) convolution2dLayer(3,32,'Padding',1) batchNormalizationLayer reluLayer maxPooling2dLayer(2,'Stride',2) convolution2dLayer(3,64,'Padding',1) batchNormalizationLayer reluLayer fullyConnectedLayer(10) softmaxLayer classificationLayer];
Define Option for Training
If you decrease initial learning rate then accuracy will be reduced and if you increase then accuracy will be increased. But be careful it can be overfitting.
options = trainingOptions('sgdm', ... 'InitialLearnRate',0.01, ... 'MaxEpochs',10, ... 'Shuffle','every-epoch', ... 'ValidationFrequency',10, ... 'Verbose',false, ... 'Plots','training-progress');
Train Network Using Training Data
CNN model will be saved as convnet
convnet = trainNetwork(imdstrain,layers,options);
The training progress plot shows the mini-batch loss and accuracy and the validation loss and accuracy.
Calaculate Accuracy using Validation Dataset
YPred = classify(convnet,imdsvalid); YValidation = imdsvalid.Labels; accuracy = sum(YPred == YValidation)/numel(YValidation)
Plot Confusion Matrix
First input will be original output and second input will be predicted output.
Read an image from datastore and predict the class
a=read(imdsvalid ); class=classify(convnet,a) imshow(a) title(string(class))
Interface with webcam or IPcamera
%camera = webcam(1); % webcam %camera = ipcam('http://192.168.225.88:8080/video'); % paste the same url as show in the IP Webcam app clear camera camera = webcam while true im = camera.snapshot; picture=rgb2gray(im);% Take a picture picture = imresize(picture,[48,48]); % Resize the picture label = classify(convnet, picture); % Classify the picture image(im); % Show the picture title(char(label)); % Show the label drawnow; end
To understand deep learning and emotion detection using CNN through recorded webinar kindly click here
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