Description of demo_multiclass_CNN.m

Comparison of multiclass classification using multiclass logistic regression and simplest possible CNN with one convolution and one mean pooling layer feeding into softmax

Note: implementations have not yet been parallelized and do not make use of GPUs in order to keep the algorithm easy to understand and extend. Consequently, this demo will take significant time to run.

% load data from MNIST
Decompressing MNIST files...Done.
Loading MNIST files into Workspace...Ready to train model

usage of multi-class logistic classification (MNIST data)

options_lg = [];

options_lg.addBias = 1;
model_lg = ml_multiclass_logistic(Xtrain, ytrain, options_lg);
yhat_lg = model_lg.predict(model_lg, Xtest);
testError_lg = mean(yhat_lg ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ..., testError_lg);
Averaged misclassification test error with Multiclass Logistic Classification is: 0.079

usage of multi-class CNN classification (MNIST data)

options_cnn.imageDim = 28;
options_cnn.nClasses = 10;
options_cnn.filterDim = 9;  % Filter size for conv layer
options_cnn.nFilters = 20;   % Number of filters for conv layer
options_cnn.poolDim = 2;
model_cnn = ml_multiclass_CNN(Xtrain, ytrain, options_cnn);
yhat_cnn = model_cnn.predict(model_cnn, Xtest);
testError_cnn = mean(yhat_cnn ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ..., testError_cnn);
Training example: 1
Training example: 10001
Training example: 20001
Training example: 30001
Training example: 40001
Training example: 50001
Training example: 60001
Training example: 70001
Training example: 80001
Training example: 90001
Averaged misclassification test error with CNN for Multiclass Image Classification is: 0.020