%temp2.m
imageInputSize = [28,28,1];
filterSize = 3;
numFilters = 8;
numClasses = 10;
numFeatures = 50;
layers = [
imageInputLayer(imageInputSize,'Normalization','none','Name','images')
convolution2dLayer(filterSize,numFilters,'Name','conv')
reluLayer('Name','relu')
fullyConnectedLayer(50,'Name','fc1')%1 x 1 x 50 x N
squeezeLayer()%50 x N
concatenationLayer(2,2,'Name','cat')
fullyConnectedLayer(numClasses,'Name','fc2')
softmaxLayer('Name','softmax')
classificationLayer];
lgraph = layerGraph(layers);
featInput = featureInputLayer(numFeatures,Name="features");%3 x N
lgraph = addLayers(lgraph,featInput);
lgraph = connectLayers(lgraph,"features","cat/in2");
numObservations = 100;
fakeImages = randn([imageInputSize,numObservations]);%28 28 1 100
imagesDS = arrayDatastore(fakeImages,IterationDimension=4);
fakeFeatures = randn([numFeatures,numObservations]);%100 x 50
featureDS = arrayDatastore(fakeFeatures,IterationDimension=2);%50x100
fakeTargets = categorical(mod(1:2*numObservations,numClasses));%1x100
targetDS = arrayDatastore(fakeTargets,IterationDimension=2);
ds = combine(imagesDS,featureDS,targetDS);
opts = trainingOptions("adam","MaxEpochs",1,"MiniBatchSize",128);
net=trainNetwork(ds,lgraph,opts);
function layer = squeezeLayer(args)
arguments
args.Name='';
end
layer = functionLayer(@squeezeLayerFcn,"Name",args.Name,"Formattable",true);
end
function x = squeezeLayerFcn(x)
x = squeeze(x);
% Since squeeze will squeeze out some dimensions, we need to relabel x.
% Assumption: x does not have a 'T' dimension.
n = ndims(x);
newdims = [repelem('S',n-2),'CB'];
x = dlarray(x,newdims);
%dims(x)
end
Error in temp2 (line 35)
net=trainNetwork(ds,lgraph,opts);
Caused by:
Layer 'cat': Input size mismatch. Size of input to this layer is different from the expected input size.
Inputs to this layer:
from layer 'layer' (size 50(C) × 1(B))
from layer 'features' (size 50(C) × 1(B))
