CUDA_ERROR_ILLEGAL_ADDRESS when runnin Faster R-CNN on Matlab

I'm running faster R-CNN in matlab 2018b on a Windows 10. I face an exception CUDA_ERROR_ILLEGAL_ADDRESS when I increase the number of my training items or when I increase the MaxEpoch.

Below are the information of my gpuDevice

  CUDADevice with properties:

                  Name: 'GeForce GTX 1050'
                 Index: 1
     ComputeCapability: '6.1'
        SupportsDouble: 1
         DriverVersion: 9.2000
        ToolkitVersion: 9.1000
    MaxThreadsPerBlock: 1024
      MaxShmemPerBlock: 49152
    MaxThreadBlockSize: [1024 1024 64]
           MaxGridSize: [2.1475e+09 65535 65535]
             SIMDWidth: 32
           TotalMemory: 4.2950e+09
       AvailableMemory: 3.4635e+09
   MultiprocessorCount: 5
          ClockRateKHz: 1493000
           ComputeMode: 'Default'
  GPUOverlapsTransfers: 1
KernelExecutionTimeout: 1
      CanMapHostMemory: 1
       DeviceSupported: 1
        DeviceSelected: 1

And this is my code

latest_index =0;

for i=1:6

load (strcat('newDataset', int2str(i), '.mat')); 
len =length(vehicleDataset.imageFilename);

for j=1:len

filename = vehicleDataset.imageFilename{j};
latest_index=latest_index+1;
fulldata.imageFilename{latest_index} = filename;
fulldata.vehicle{latest_index} = vehicleDataset.vehicle{j};

end
end 

trainingDataTable = table(fulldata.imageFilename', fulldata.vehicle');
trainingDataTable.Properties.VariableNames = {'imageFilename','vehicle'};

data.trainingDataTable = trainingDataTable;
trainingDataTable(1:4,:)


% Split data into a training and test set.
idx = floor(0.6 * height(trainingDataTable));

trainingData = trainingDataTable(1:idx,:);
testData = trainingDataTable(idx:end,:);

% Create image input layer.
inputLayer = imageInputLayer([32 32 3]);

% Define the convolutional layer parameters.
filterSize = [3 3];

numFilters = 64;

% Create the middle layers.
middleLayers = [

convolution2dLayer(filterSize, numFilters, 'Padding', 1)   
reluLayer()
convolution2dLayer(filterSize, numFilters, 'Padding', 1)  
reluLayer() 
maxPooling2dLayer(3, 'Stride',2)
];

finalLayers = [

fullyConnectedLayer(128)
% Add a ReLU non-linearity.
reluLayer()

fullyConnectedLayer(width(trainingDataTable))
% Add the softmax loss layer and classification layer. 
softmaxLayer()
classificationLayer()
];

layers = [
inputLayer
middleLayers
finalLayers
];

% Options for step 1.
 optionsStage1 = trainingOptions('sgdm', ...
 'MaxEpochs', 2, ...
 'MiniBatchSize', 1, ...
 'InitialLearnRate', 1e-3, ...
 'CheckpointPath', tempdir);

% Options for step 2.
optionsStage2 = trainingOptions('sgdm', ...
'MaxEpochs', 2, ...
'MiniBatchSize', 1, ...
'InitialLearnRate', 1e-3, ...
'CheckpointPath', tempdir);

% Options for step 3.
optionsStage3 = trainingOptions('sgdm', ...
'MaxEpochs', 2, ...
'MiniBatchSize', 1, ...    
'InitialLearnRate', 1e-3, ...
'CheckpointPath', tempdir);

% Options for step 4.
optionsStage4 = trainingOptions('sgdm', ...
'MaxEpochs', 2, ...
'MiniBatchSize', 1, ...
'InitialLearnRate', 1e-3, ...
'CheckpointPath', tempdir);

 options = [
 optionsStage1
 optionsStage2
 optionsStage3
 optionsStage4
 ];

 doTrainingAndEval = true;

if doTrainingAndEval
 % Set random seed to ensure example training reproducibility.
 rng(0);

 % Train Faster R-CNN detector. Select a BoxPyramidScale of 1.2 to allow
 % for finer resolution for multiscale object detection.
 detector = trainFasterRCNNObjectDetector(trainingData, layers, options, ...
    'NegativeOverlapRange', [0 0.3], ...
    'PositiveOverlapRange', [0.6 1], ...
    'BoxPyramidScale', 1.2);

 data.detector= detector;
else

% Load pretrained detector for the example.
detector = data.detector;

end

save mix_data data

if doTrainingAndEval

% Run detector on each image in the test set and collect results.

resultsStruct = struct([]);

for i = 1:height(testData)
 % Read the image.
    I = imread(testData.imageFilename{i});
    % Run the detector.
    [bboxes, scores, labels] = detect(detector, I);

    % Collect the results.
    resultsStruct(i).Boxes = bboxes;
    resultsStruct(i).Scores = scores;
    resultsStruct(i).Labels = labels;
end

% Convert the results into a table.
results = struct2table(resultsStruct);

data.results = results;

save mix_data data

else

% Load results from disk.
results = data.results;
end

% Extract expected bounding box locations from test data.
expectedResults = testData(:, 2:end);

% Evaluate the object detector using Average Precision metric.
[ap, recall, precision] = evaluateDetectionPrecision(results, expectedResults);

% Plot precision/recall curve
figure
plot(recall,precision)

xlabel('Recall')
ylabel('Precision')

grid on
title(sprintf('Average Precision = %.2f', ap))

First it prints the warning multiple time and throws the below exception

Warning: An unexpected error occurred during CUDA execution. The CUDA error was: CUDA_ERROR_ILLEGAL_ADDRESS In trainFasterRCNNObjectDetector (line 320) In rcnn_trail (line 184)

Error using - An unexpected error occurred during CUDA execution. The CUDA error was: CUDA_ERROR_ILLEGAL_ADDRESS

Error in vision.internal.cnn.layer.SmoothL1Loss/backwardLoss (line 156) idx = (X > -one) & (X < one); Error in nnet.internal.cnn.DAGNetwork/computeGradientsForTraining/efficientBackProp (line 585) dLossdX = thisLayer.backwardLoss( ...

Error in nnet.internal.cnn.DAGNetwork>@()efficientBackProp(i) (line 661) @() efficientBackProp(i), ...

Error in nnet.internal.cnn.util.executeWithStagedGPUOOMRecovery (line 11) [ varargout{1:nOutputs} ] = computeFun();

Error in nnet.internal.cnn.DAGNetwork>iExecuteWithStagedGPUOOMRecovery (line 1195) [varargout{1:nargout}] = nnet.internal.cnn.util.executeWithStagedGPUOOMRecovery(varargin{:});

Error in nnet.internal.cnn.DAGNetwork/computeGradientsForTraining (line 660) theseGradients = iExecuteWithStagedGPUOOMRecovery( ...

Error in nnet.internal.cnn.Trainer/computeGradients (line 184) [gradients, predictions, states] = net.computeGradientsForTraining(X, Y, needsStatefulTraining, propagateState);

Error in nnet.internal.cnn.Trainer/train (line 85) [gradients, predictions, states] = this.computeGradients(net, X, response, needsStatefulTraining, propagateState);

Error in vision.internal.cnn.trainNetwork (line 47) trainedNet = trainer.train(trainedNet, trainingDispatcher);

Error in fastRCNNObjectDetector.train (line 190) [network, info] = vision.internal.cnn.trainNetwork(ds, lgraph, opts, mapping, checkpointSaver);

Error in trainFasterRCNNObjectDetector (line 410) [stage2Detector, fastRCNN, ~, info(2)] = fastRCNNObjectDetector.train(trainingData, fastRCNN, options(2), iStageTwoParams(params), checkpointSaver);

Error in rcnn_trail (line 184) detector = trainFasterRCNNObjectDetector(trainingData, layers, options, ...