Convolutional neural networks usefeaturesto classify images. The network learns these features itself during the training process. What the network learns during training is sometimes unclear. However, you can
Neural Network Toolbox™ provides algorithms, pretrained models, and apps to create, train, visualize, and simulate both shallow and deep neural networks. You can perform classification,regression, clustering,dimensionality reduction, time-seriesforecasting, and dynamic system modeling and control. Deep ...
Interactively Build, Visualize, and Edit Deep Learning Networks You can also select a web site from the following list How to Get Best Site Performance Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your ...
% You can now "visualize" what the neural network is learning by % displaying the hidden units to see what features they are capturing in % the data. fprintf('\nVisualizing Neural Network... \n') displayData(Theta1(:, 2:end)); fprintf('\nProgram paused. Press enter to continue.\n'...
This MATLAB function returns the classification edge for the trained neural network classifier Mdl using the predictor data in table Tbl and the class labels in the ResponseVarName table variable.
(generatorHighDoseToLowDose,imageTestHD); % Display a few images to visualize the network responses if ismember(idx,idxImagesToDisplay) figure origImLD = rescale(extractdata(imageTestLD)); genImHD = rescale(extractdata(generatedImageHD)); montage({origImLD,genImHD},Size=[1 2],BorderSize...
% You can now "visualize" what the neural network is learning by % displaying the hidden units to see what features they are capturing in % the data. fprintf('\nVisualizing Neural Network... \n') displayData(Theta1(:, 2:end)); ...
% You can visualize the results with a confusion matrix. The numbers in the % bottom right-hand square of the matrix give the overall accuracy. y = deepnet(xTest); plotconfusion(tTest,y); %% Fine tuning the deep neural network % The results for the deep neural network can be improved ...
easily modeled with a closed-form equation. Neural Network Toolbox supports supervised learning with feedforward, radial basis, and dynamic networks. It also supports unsupervised learning with self-organizing maps and competitive layers. With the toolbox you can design, train, visualize, and simulate...
The names of the network layers to expand, specified as a character vector, string scalar, or string array. Example:"subnet_1" Example:["subnet_1" "subnet_2"] Data Types:char|string The indices of the network layers to expand, specified as an integer or vector of integers. ...