GOT AN ERROR ON VGG19 FULLYCONNECTED LAYER

Dear All

Anyone can help me to solve the problem?

Below is my code for VGG19 net.

I have 75 folder data. In each folder have 5 set images. every images have size 240x240x155.

But I got error below

Error using trainNetwork
Layer 39: Invalid initializer. Requested 75x285696000 (159.6GB) array exceeds maximum array size preference (15.9GB). This might
cause MATLAB to become unresponsive.

Caused by:
Error using nnet.internal.cnn.assembler.InitializeMixin/initializeLearnableParameters
Layer 39: Invalid initializer. Requested 75x285696000 (159.6GB) array exceeds maximum array size preference (15.9GB). This
might cause MATLAB to become unresponsive.

Related documentation

below is my code

clc
clear all
close all

%testDataimages
volReader = @(x) niftiread(x);
volds = imageDatastore('C:\Users\USER\Downloads\LGG',...
    'IncludeSubfolders', true,...
    'LabelSource', 'foldernames','FileExtensions','.nii','ReadFcn',volReader);

layers = [
    image3dInputLayer([240 240 155],"Name","image3dinput")
    convolution3dLayer([3 3 3],32,"Name","conv3d_1","Padding","same")
    reluLayer("Name","relu_1")
    convolution3dLayer([3 3 3],32,"Name","conv3d_2","Padding","same")
    reluLayer("Name","relu_2")
    maxPooling3dLayer([5 5 5],"Name","maxpool3d_1","Padding","same")
    convolution3dLayer([3 3 3],32,"Name","conv3d_3","Padding","same")
    reluLayer("Name","relu_3")
    convolution3dLayer([3 3 3],32,"Name","conv3d_4","Padding","same")
    reluLayer("Name","relu_4")
    maxPooling3dLayer([5 5 5],"Name","maxpool3d_2","Padding","same")
    convolution3dLayer([3 3 3],32,"Name","conv3d_5","Padding","same")
    reluLayer("Name","relu_5")
    convolution3dLayer([3 3 3],32,"Name","conv3d_6","Padding","same")
    reluLayer("Name","relu_6")
    convolution3dLayer([3 3 3],32,"Name","conv3d_7","Padding","same")
    reluLayer("Name","relu_7")
    convolution3dLayer([3 3 3],32,"Name","conv3d_8","Padding","same")
    reluLayer("Name","relu_8")
    maxPooling3dLayer([5 5 5],"Name","maxpool3d_3","Padding","same")
    convolution3dLayer([3 3 3],32,"Name","conv3d_9","Padding","same")
    reluLayer("Name","relu_9")
    convolution3dLayer([3 3 3],32,"Name","conv3d_10","Padding","same")
    reluLayer("Name","relu_10")
    convolution3dLayer([3 3 3],32,"Name","conv3d_11","Padding","same")
    reluLayer("Name","relu_11")
    convolution3dLayer([3 3 3],32,"Name","conv3d_12","Padding","same")
    reluLayer("Name","relu_12")
    maxPooling3dLayer([5 5 5],"Name","maxpool3d_4","Padding","same")
    convolution3dLayer([3 3 3],32,"Name","conv3d_13","Padding","same")
    reluLayer("Name","relu_13")
    convolution3dLayer([3 3 3],32,"Name","conv3d_14","Padding","same")
    reluLayer("Name","relu_14")
    convolution3dLayer([3 3 3],32,"Name","conv3d_15","Padding","same")
    reluLayer("Name","relu_15")
    convolution3dLayer([3 3 3],32,"Name","conv3d_16","Padding","same")
    reluLayer("Name","relu_16")
    maxPooling3dLayer([5 5 5],"Name","maxpool3d_5","Padding","same")
    fullyConnectedLayer(75,"Name","fc_3")
    reluLayer("Name","relu_18")
    dropoutLayer(0.5,"Name","dropout_1")
    fullyConnectedLayer(75,"Name","fc_2")
    reluLayer("Name","relu_17")
    dropoutLayer(0.5,"Name","dropout_2")
    fullyConnectedLayer(75,"Name","fc_1")
    softmaxLayer("Name","softmax")
    pixelClassificationLayer("Name","pixel-class")];

plot(layerGraph(layers));


% Train network  - 10000 epochs and 10 Augmentations per mask.
maxEpochs = 200;
options = trainingOptions('adam', ...
    'MaxEpochs',maxEpochs, ...
    'InitialLearnRate',1e-3, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropPeriod',5, ...
    'LearnRateDropFactor',0.97, ...
    'Plots','training-progress', ...
    'Verbose',false);
% 		'CheckpointPath', checkpointPath );


[convnet, traininfo] = trainNetwork(volds, layers, options);