This is post #2. First one is about creating dataset and the last one is about using created network for shapes classification.

Transfer learning is commonly used by deep learning applications. In practice, 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. For this example we will use Image dataset, that contains 4 labels: circle, rectangle, triangle and star, created with this script.

imds = imageDatastore('imgs_shapes', ... %Load image from folder
    'IncludeSubfolders',true, ... %Load also from subflder
    'LabelSource','foldernames'); %Use label source same as the file names 
%Split dataset on training and validation
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');

%Load pretrained neaural network
net = alexnet;

%Last 3 layers will be replaced 
inputSize = net.Layers(1).InputSize; %input size
layersTransfer = net.Layers(1:end-3);
numClasses = numel(categories(imdsTrain.Labels)); %Label count

%Final layers for our new neural network
layers = [
    layersTransfer
    fullyConnectedLayer(numClasses,'WeightLearnRateFactor',20,'BiasLearnRateFactor',20)
    softmaxLayer
    classificationLayer];

%Train Network
%Options for Training
options = trainingOptions('sgdm', ...
    'MiniBatchSize',10, ...
    'MaxEpochs',12, ... 
    'InitialLearnRate',1e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');

%Start training of our neural network with transfered and modified layers
netTransfer = trainNetwork(imdsTrain,layers,options);