diff --git a/code/nnv/engine/nn/NN.m b/code/nnv/engine/nn/NN.m
index b7f10d78ba..5e2977d6aa 100644
--- a/code/nnv/engine/nn/NN.m
+++ b/code/nnv/engine/nn/NN.m
@@ -237,13 +237,15 @@
             if strcmp(obj.dis_opt, 'display')
                 fprintf('\nPerform reachability analysis for the network %s \n', obj.Name);
             end
+
+            % ensure NN parameters and input set share same precision
+            inputSet = obj.consistentPrecision(inputSet); % change only input, this can be changed in the future
             
             % Perform reachability based on connections or assume no skip/sparse connections
             if isempty(obj.Connections)
                 outputSet = obj.reach_noConns(inputSet);
             else 
                 outputSet = obj.reach_withConns(inputSet);
-            
             end
 
         end
@@ -945,6 +947,25 @@
                 reachOptions.numCores = 1;
             end
         end
+        
+        % Ensure input and parameter precision is the same
+        function inputSet = consistentPrecision(obj, inputSet)
+            % (assume parameters have same precision across layers)
+            % approach: change input precision based on network parameters
+            inputPrecision = class(inputSet.V);
+            netPrecision = 'double'; % default
+            for i=1:length(obj.Layers)
+                if isa(obj.Layers{i}, "FullyConnectedLayer") || isa(obj.Layers{i}, "Conv2DLayer")
+                    netPrecision = class(obj.Layers{i}.Weights);
+                    break;
+                end
+            end
+            if ~strcmp(inputPrecision, netPrecision)
+                % input and parameter precision does not match
+                warning("Changing input set precision to "+string(netPrecision));
+                inputSet = inputSet.changeVarsPrecision(netPrecision);
+            end
+        end
 
         % Create input set based on input vector and bounds
         function R = create_input_set(obj, x_in, disturbance, lb_allowable, ub_allowable) % assume tol is applied to every vale of the input
diff --git a/code/nnv/engine/nn/funcs/TanSig.m b/code/nnv/engine/nn/funcs/TanSig.m
index 1664f7159f..36fee9785e 100644
--- a/code/nnv/engine/nn/funcs/TanSig.m
+++ b/code/nnv/engine/nn/funcs/TanSig.m
@@ -59,7 +59,7 @@
             if ~isa(I, 'Star')
                 error('Input set is not a star set');
             end            
-            if strcmp(method, 'approx-star-no-split') || strcmp(method, 'approx-star')
+            if strcmp(method, 'approx-star-no-split') || strcmp(method, 'approx-star') || contains(method, 'relax-star')
                 if relaxFactor == 0
                     S = TanSig.multiStepTanSig_NoSplit(I, dis_opt, lp_solver);
                 else
diff --git a/code/nnv/engine/set/ImageStar.m b/code/nnv/engine/set/ImageStar.m
index 8bdc676bcc..9e005d0bec 100644
--- a/code/nnv/engine/set/ImageStar.m
+++ b/code/nnv/engine/set/ImageStar.m
@@ -656,6 +656,30 @@
             
         end
         
+        % change variable precision
+        function S = changeVarsPrecision(obj, precision)
+            S = obj;
+            if strcmp(precision, 'single')
+                S.V = single(S.V);
+                S.C = single(S.C);
+                S.d = single(S.d);
+                S.pred_lb = single(S.pred_lb); 
+                S.pred_ub = single(S.pred_lb);
+                S.im_lb = single(S.im_lb); 
+                S.im_ub = single(S.im_ub);
+            elseif strcmp(precision, 'double')
+                S.V = double(S.V);
+                S.C = double(S.C);
+                S.d = double(S.d);
+                S.pred_lb = double(S.pred_lb); 
+                S.pred_ub = double(S.pred_lb);
+                S.im_lb = double(S.im_lb); 
+                S.im_ub = double(S.im_ub);
+            else
+                error("Only single or double precision arrays allowed. GpuArray/dlarray are coming.")
+            end
+        end
+
     end
 
 
diff --git a/code/nnv/engine/set/ImageZono.m b/code/nnv/engine/set/ImageZono.m
index a3ca88f01b..111bad7316 100644
--- a/code/nnv/engine/set/ImageZono.m
+++ b/code/nnv/engine/set/ImageZono.m
@@ -280,7 +280,22 @@
             S = obj.toImageStar;
             S = S.toStar;
         end
-
+        
+        % change variable precision
+        function S = changeVarsPrecision(obj, precision)
+            S = obj;
+            if strcmp(precision, 'single')
+                S.V = single(S.V);
+                S.lb_image = single(S.lb_image); 
+                S.ub_image = single(S.ub_image);
+            elseif strcmp(precision, 'double')
+                S.V = double(S.V);
+                S.lb_image = double(S.lb_image); 
+                S.ub_image = double(S.ub_image);
+            else
+                error("Only single or double precision arrays allowed. GpuArray/dlarray are coming.")
+            end
+        end
     end
   
        
diff --git a/code/nnv/engine/set/Star.m b/code/nnv/engine/set/Star.m
index bdc91248f1..f02941c02e 100644
--- a/code/nnv/engine/set/Star.m
+++ b/code/nnv/engine/set/Star.m
@@ -1448,6 +1448,30 @@
                         
         end
         
+        % change variable precision
+        function S = changeVarsPrecision(obj, precision)
+            S = obj;
+            if strcmp(precision, 'single')
+                S.V = single(S.V);
+                S.C = single(S.C);
+                S.d = single(S.d);
+                S.predicate_lb = single(S.predicate_lb); 
+                S.predicate_ub = single(S.predicate_lb);
+                S.state_lb = single(S.state_lb); 
+                S.state_ub = single(S.state_ub);
+            elseif strcmp(precision, 'double')
+                S.V = double(S.V);
+                S.C = double(S.C);
+                S.d = double(S.d);
+                S.predicate_lb = double(S.predicate_lb); 
+                S.predicate_ub = double(S.predicate_lb);
+                S.state_lb = double(S.state_lb); 
+                S.state_ub = double(S.state_ub);
+            else
+                error("Only single or double precision arrays allowed. GpuArray/dlarray are coming.")
+            end
+        end
+
     end
     
 
@@ -1526,8 +1550,14 @@ function plot(varargin)
                         end
                     end
                 else
-                    P = obj.toPolyhedron;
-                    P.plot('color', color);
+                    if isa(obj.V, 'single') || isa(obj.C,'single') || isa(obj.d, 'single')
+                        S = obj.changeVarsPrecision('double');
+                        P = S.toPolyhedron;
+                        P.plot('color', color);
+                    else
+                        P = obj.toPolyhedron;
+                        P.plot('color', color);
+                    end
                 end
             
             else
diff --git a/code/nnv/engine/set/VolumeStar.m b/code/nnv/engine/set/VolumeStar.m
index db2a0d396b..bec37d75c2 100644
--- a/code/nnv/engine/set/VolumeStar.m
+++ b/code/nnv/engine/set/VolumeStar.m
@@ -523,7 +523,30 @@
             
         end
         
-        % TODO: add a 3D projection to ImageStar
+        % change variable precision
+        function S = changeVarsPrecision(obj, precision)
+            S = obj;
+            if strcmp(precision, 'single')
+                S.V = single(S.V);
+                S.C = single(S.C);
+                S.d = single(S.d);
+                S.pred_lb = single(S.pred_lb); 
+                S.pred_ub = single(S.pred_lb);
+                S.vol_lb = single(S.vol_lb); 
+                S.vol_ub = single(S.vol_ub);
+            elseif strcmp(precision, 'double')
+                S.V = double(S.V);
+                S.C = double(S.C);
+                S.d = double(S.d);
+                S.pred_lb = double(S.pred_lb); 
+                S.pred_ub = double(S.pred_lb);
+                S.vol_lb = double(S.vol_lb); 
+                S.vol_ub = double(S.vol_ub);
+            else
+                error("Only single or double precision arrays allowed. GpuArray/dlarray are coming.")
+            end
+        end
+
     end
 
 
diff --git a/code/nnv/engine/set/Zono.m b/code/nnv/engine/set/Zono.m
index 5745181d67..7970d0d19a 100644
--- a/code/nnv/engine/set/Zono.m
+++ b/code/nnv/engine/set/Zono.m
@@ -343,6 +343,20 @@
             imageStar = im1.toImageStar(height, width, numChannels);
             
         end
+        
+        % change variable precision
+        function S = changeVarsPrecision(obj, precision)
+            S = obj;
+            if strcmp(precision, 'single')
+                S.V = single(S.V);
+                S.c = single(S.c);
+            elseif strcmp(precision, 'double')
+                S.V = double(S.V);
+                S.c = double(S.c);
+            else
+                error("Only single or double precision arrays allowed. GpuArray/dlarray are coming.")
+            end
+        end
 
     end
 
diff --git a/code/nnv/engine/utils/export2vnnlib.m b/code/nnv/engine/utils/export2vnnlib.m
index 03e1b29d83..624a2eecdf 100644
--- a/code/nnv/engine/utils/export2vnnlib.m
+++ b/code/nnv/engine/utils/export2vnnlib.m
@@ -106,7 +106,10 @@ function export2vnnlib(lb, ub, outsize, property, name)
     % Outputs a string to write in the vnnlib file
 
     locs = find(hRow ~= 0); % Find indexes that are not zero
-    if hVal == 0 % Compare two indexes
+    if length(locs) > 1
+        if hVal ~= 0 % Compare two indexes
+            error("Only allowed index to index comparison, or 1 index to value, but not both.")
+        end
         if hRow(locs(1)) > 0 % 
             str = "(>= Y_"+string(locs(2)-1) + " " + "Y_"+string(locs(1)-1)+ "))";
         else
diff --git a/code/nnv/engine/utils/load_vnnlib.m b/code/nnv/engine/utils/load_vnnlib.m
index cbb3b5e2ba..c850736151 100644
--- a/code/nnv/engine/utils/load_vnnlib.m
+++ b/code/nnv/engine/utils/load_vnnlib.m
@@ -35,8 +35,8 @@
                 dim = dim + 1; % only have seen inputs defined as vectors, so this should work
                 % the more general approach would require some extra work, but should be easy as well
             elseif contains(tline, "declare-const") && contains(tline, "Y_")
-                lb_template = zeros(dim,1);
-                ub_template = zeros(dim,1);
+                lb_template = zeros(dim,1,'single');
+                ub_template = zeros(dim,1,'single');
                 dim = 0; % reset dimension counter
                 phase = "DeclareOutput";
                 continue;  % redo this line in correct phase
@@ -51,7 +51,7 @@
                 dim = 1; % reset dimension counter
                 phase = "DefineInput";
                 % Initialize variables
-                 lb_input = lb_template;
+                lb_input = lb_template;
                 ub_input = ub_template;
                 continue;  % redo this line in correct phase
             end
@@ -240,7 +240,7 @@
             H(idx2) = -1;
         else
             var2 = split(var2, ')');
-            g = str2double(var2{1});
+            g = single(str2double(var2{1}));
         end
     else
         H(idx1) = -1;
@@ -252,7 +252,7 @@
             H(idx2) = 1;
         else
             var2 = split(var2, ')');
-            g = -str2double(var2{1});
+            g = -single(str2double(var2{1}));
         end
     end
     % Add constraint (H, g) to assertion variable (ast)
@@ -346,7 +346,7 @@
         dim = split(t{2},'_');
         dim = str2double(dim{2})+1;
         value = split(t{3},')');
-        value = str2double(value{1});
+        value = single(str2double(value{1}));
         if contains(t{1},">=")
             lb_input(dim) = value;
         else
@@ -374,7 +374,7 @@
             if contains(x, 'X')
                 [lb_input, ub_input] =  process_input_constraint(x, lb_input, ub_input);
             else
-                [H, g] =  process_output_combo_constraint(tline, H, g, output_dim);
+                [H, g] =  process_output_combo_constraint(x, H, g, output_dim);
             end
         end
         Hg = HalfSpace(H,g);
@@ -401,7 +401,7 @@
         dim = split(t{2},'_');
         dim = str2double(dim{2})+1;
         value = split(t{3},')');
-        value = str2double(value{1});
+        value = single(str2double(value{1}));
         if contains(t{1},">=") || contains(t{1}, ">")
             Hvec(dim) = -1;
             gval = -value;
diff --git a/code/nnv/examples/NN/medmnist/multipleAttacks_3D_results.txt b/code/nnv/examples/NN/medmnist/multipleAttacks_3D_results.txt
index 9922e6425f..7664b23526 100644
--- a/code/nnv/examples/NN/medmnist/multipleAttacks_3D_results.txt
+++ b/code/nnv/examples/NN/medmnist/multipleAttacks_3D_results.txt
@@ -1,5 +1,23 @@
 *******************************************************
 ================= PROCESSING RESULTS: results/verification_multipleAttacks_adrenalmnist3d.mat ...
+... processing dark attack  with 50 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  152
+Number of not robust images      =  0
+Number of unknown images         =  10
+Number of missclassified images  =  38
+Average computation time of 0.089655
+ 
+... processing dark attack  with 50 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  152
+Number of not robust images      =  0
+Number of unknown images         =  10
+Number of missclassified images  =  38
+Average computation time of 0.091389
+ 
 ... processing dark attack  with 50 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -9,6 +27,24 @@ Number of unknown images         =  10
 Number of missclassified images  =  38
 Average computation time of 0.097665
  
+... processing dark attack  with 100 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  152
+Number of not robust images      =  0
+Number of unknown images         =  10
+Number of missclassified images  =  38
+Average computation time of 0.094814
+ 
+... processing dark attack  with 100 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  152
+Number of not robust images      =  0
+Number of unknown images         =  10
+Number of missclassified images  =  38
+Average computation time of 0.088423
+ 
 ... processing dark attack  with 100 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -18,6 +54,24 @@ Number of unknown images         =  10
 Number of missclassified images  =  38
 Average computation time of 0.093128
  
+... processing dark attack  with 200 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  152
+Number of not robust images      =  0
+Number of unknown images         =  10
+Number of missclassified images  =  38
+Average computation time of 0.082751
+ 
+... processing dark attack  with 200 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  152
+Number of not robust images      =  0
+Number of unknown images         =  10
+Number of missclassified images  =  38
+Average computation time of 0.090661
+ 
 ... processing dark attack  with 200 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -27,6 +81,24 @@ Number of unknown images         =  10
 Number of missclassified images  =  38
 Average computation time of 0.10202
  
+... processing bright attack  with 50 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  160
+Number of not robust images      =  0
+Number of unknown images         =  2
+Number of missclassified images  =  38
+Average computation time of 0.10423
+ 
+... processing bright attack  with 50 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  160
+Number of not robust images      =  0
+Number of unknown images         =  2
+Number of missclassified images  =  38
+Average computation time of 0.21532
+ 
 ... processing bright attack  with 50 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -36,6 +108,24 @@ Number of unknown images         =  2
 Number of missclassified images  =  38
 Average computation time of 0.23374
  
+... processing bright attack  with 100 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  160
+Number of not robust images      =  0
+Number of unknown images         =  2
+Number of missclassified images  =  38
+Average computation time of 0.15527
+ 
+... processing bright attack  with 100 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  160
+Number of not robust images      =  0
+Number of unknown images         =  2
+Number of missclassified images  =  38
+Average computation time of 0.15155
+ 
 ... processing bright attack  with 100 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -45,6 +135,24 @@ Number of unknown images         =  2
 Number of missclassified images  =  38
 Average computation time of 0.19122
  
+... processing bright attack  with 200 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  157
+Number of not robust images      =  0
+Number of unknown images         =  5
+Number of missclassified images  =  38
+Average computation time of 0.15655
+ 
+... processing bright attack  with 200 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  157
+Number of not robust images      =  0
+Number of unknown images         =  5
+Number of missclassified images  =  38
+Average computation time of 0.24745
+ 
 ... processing bright attack  with 200 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -57,6 +165,24 @@ Average computation time of 0.26513
 *******************************************************
 *******************************************************
 ================= PROCESSING RESULTS: results/verification_multipleAttacks_fracturemnist3d.mat ...
+... processing dark attack  with 50 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  64
+Number of not robust images      =  0
+Number of unknown images         =  21
+Number of missclassified images  =  115
+Average computation time of 0.066963
+ 
+... processing dark attack  with 50 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  64
+Number of not robust images      =  0
+Number of unknown images         =  21
+Number of missclassified images  =  115
+Average computation time of 0.071886
+ 
 ... processing dark attack  with 50 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -66,6 +192,24 @@ Number of unknown images         =  21
 Number of missclassified images  =  115
 Average computation time of 0.077774
  
+... processing dark attack  with 100 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  62
+Number of not robust images      =  0
+Number of unknown images         =  23
+Number of missclassified images  =  115
+Average computation time of 0.070278
+ 
+... processing dark attack  with 100 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  62
+Number of not robust images      =  0
+Number of unknown images         =  23
+Number of missclassified images  =  115
+Average computation time of 0.07056
+ 
 ... processing dark attack  with 100 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -75,6 +219,24 @@ Number of unknown images         =  23
 Number of missclassified images  =  115
 Average computation time of 0.084388
  
+... processing dark attack  with 200 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  61
+Number of not robust images      =  0
+Number of unknown images         =  24
+Number of missclassified images  =  115
+Average computation time of 0.062923
+ 
+... processing dark attack  with 200 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  61
+Number of not robust images      =  0
+Number of unknown images         =  24
+Number of missclassified images  =  115
+Average computation time of 0.068888
+ 
 ... processing dark attack  with 200 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -84,6 +246,24 @@ Number of unknown images         =  24
 Number of missclassified images  =  115
 Average computation time of 0.078273
  
+... processing bright attack  with 50 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  85
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  115
+Average computation time of 0.11543
+ 
+... processing bright attack  with 50 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  85
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  115
+Average computation time of 0.25427
+ 
 ... processing bright attack  with 50 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -93,6 +273,24 @@ Number of unknown images         =  0
 Number of missclassified images  =  115
 Average computation time of 0.27232
  
+... processing bright attack  with 100 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  84
+Number of not robust images      =  0
+Number of unknown images         =  1
+Number of missclassified images  =  115
+Average computation time of 0.081964
+ 
+... processing bright attack  with 100 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  84
+Number of not robust images      =  0
+Number of unknown images         =  1
+Number of missclassified images  =  115
+Average computation time of 0.20942
+ 
 ... processing bright attack  with 100 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -102,6 +300,24 @@ Number of unknown images         =  1
 Number of missclassified images  =  115
 Average computation time of 0.23118
  
+... processing bright attack  with 200 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  80
+Number of not robust images      =  0
+Number of unknown images         =  5
+Number of missclassified images  =  115
+Average computation time of 0.081765
+ 
+... processing bright attack  with 200 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  80
+Number of not robust images      =  0
+Number of unknown images         =  5
+Number of missclassified images  =  115
+Average computation time of 0.20911
+ 
 ... processing bright attack  with 200 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -114,6 +330,24 @@ Average computation time of 0.22384
 *******************************************************
 *******************************************************
 ================= PROCESSING RESULTS: results/verification_multipleAttacks_nodulemnist3d.mat ...
+... processing dark attack  with 50 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  90
+Number of not robust images      =  0
+Number of unknown images         =  83
+Number of missclassified images  =  27
+Average computation time of 0.091647
+ 
+... processing dark attack  with 50 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  90
+Number of not robust images      =  0
+Number of unknown images         =  83
+Number of missclassified images  =  27
+Average computation time of 0.10601
+ 
 ... processing dark attack  with 50 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -123,6 +357,24 @@ Number of unknown images         =  83
 Number of missclassified images  =  27
 Average computation time of 0.11423
  
+... processing dark attack  with 100 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  80
+Number of not robust images      =  0
+Number of unknown images         =  93
+Number of missclassified images  =  27
+Average computation time of 0.094546
+ 
+... processing dark attack  with 100 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  80
+Number of not robust images      =  0
+Number of unknown images         =  93
+Number of missclassified images  =  27
+Average computation time of 0.1101
+ 
 ... processing dark attack  with 100 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -132,6 +384,24 @@ Number of unknown images         =  93
 Number of missclassified images  =  27
 Average computation time of 0.12654
  
+... processing dark attack  with 200 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  81
+Number of not robust images      =  0
+Number of unknown images         =  92
+Number of missclassified images  =  27
+Average computation time of 0.10503
+ 
+... processing dark attack  with 200 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  81
+Number of not robust images      =  0
+Number of unknown images         =  92
+Number of missclassified images  =  27
+Average computation time of 0.116
+ 
 ... processing dark attack  with 200 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -141,6 +411,24 @@ Number of unknown images         =  92
 Number of missclassified images  =  27
 Average computation time of 0.13058
  
+... processing bright attack  with 50 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  161
+Number of not robust images      =  0
+Number of unknown images         =  12
+Number of missclassified images  =  27
+Average computation time of 0.10913
+ 
+... processing bright attack  with 50 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  161
+Number of not robust images      =  0
+Number of unknown images         =  12
+Number of missclassified images  =  27
+Average computation time of 0.1208
+ 
 ... processing bright attack  with 50 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -150,6 +438,24 @@ Number of unknown images         =  12
 Number of missclassified images  =  27
 Average computation time of 0.14372
  
+... processing bright attack  with 100 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  138
+Number of not robust images      =  0
+Number of unknown images         =  35
+Number of missclassified images  =  27
+Average computation time of 0.10546
+ 
+... processing bright attack  with 100 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  138
+Number of not robust images      =  0
+Number of unknown images         =  35
+Number of missclassified images  =  27
+Average computation time of 0.12612
+ 
 ... processing bright attack  with 100 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -159,6 +465,24 @@ Number of unknown images         =  35
 Number of missclassified images  =  27
 Average computation time of 0.14722
  
+... processing bright attack  with 200 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  116
+Number of not robust images      =  0
+Number of unknown images         =  57
+Number of missclassified images  =  27
+Average computation time of 0.11492
+ 
+... processing bright attack  with 200 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  116
+Number of not robust images      =  0
+Number of unknown images         =  57
+Number of missclassified images  =  27
+Average computation time of 0.14412
+ 
 ... processing bright attack  with 200 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -171,6 +495,24 @@ Average computation time of 0.17602
 *******************************************************
 *******************************************************
 ================= PROCESSING RESULTS: results/verification_multipleAttacks_organmnist3d.mat ...
+... processing dark attack  with 50 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  2
+Number of not robust images      =  0
+Number of unknown images         =  173
+Number of missclassified images  =  25
+Average computation time of 0.14054
+ 
+... processing dark attack  with 50 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  2
+Number of not robust images      =  0
+Number of unknown images         =  173
+Number of missclassified images  =  25
+Average computation time of 0.14117
+ 
 ... processing dark attack  with 50 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -180,6 +522,24 @@ Number of unknown images         =  173
 Number of missclassified images  =  25
 Average computation time of 0.14552
  
+... processing dark attack  with 100 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  2
+Number of not robust images      =  0
+Number of unknown images         =  173
+Number of missclassified images  =  25
+Average computation time of 0.13693
+ 
+... processing dark attack  with 100 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  2
+Number of not robust images      =  0
+Number of unknown images         =  173
+Number of missclassified images  =  25
+Average computation time of 0.13875
+ 
 ... processing dark attack  with 100 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -189,6 +549,24 @@ Number of unknown images         =  173
 Number of missclassified images  =  25
 Average computation time of 0.14997
  
+... processing dark attack  with 200 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  2
+Number of not robust images      =  0
+Number of unknown images         =  173
+Number of missclassified images  =  25
+Average computation time of 0.1751
+ 
+... processing dark attack  with 200 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  2
+Number of not robust images      =  0
+Number of unknown images         =  173
+Number of missclassified images  =  25
+Average computation time of 0.162
+ 
 ... processing dark attack  with 200 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -198,6 +576,24 @@ Number of unknown images         =  173
 Number of missclassified images  =  25
 Average computation time of 0.16166
  
+... processing bright attack  with 50 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  28
+Number of not robust images      =  0
+Number of unknown images         =  147
+Number of missclassified images  =  25
+Average computation time of 0.15954
+ 
+... processing bright attack  with 50 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  28
+Number of not robust images      =  0
+Number of unknown images         =  147
+Number of missclassified images  =  25
+Average computation time of 0.17367
+ 
 ... processing bright attack  with 50 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -207,6 +603,24 @@ Number of unknown images         =  147
 Number of missclassified images  =  25
 Average computation time of 0.18946
  
+... processing bright attack  with 100 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  23
+Number of not robust images      =  0
+Number of unknown images         =  152
+Number of missclassified images  =  25
+Average computation time of 0.15496
+ 
+... processing bright attack  with 100 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  23
+Number of not robust images      =  0
+Number of unknown images         =  152
+Number of missclassified images  =  25
+Average computation time of 0.19699
+ 
 ... processing bright attack  with 100 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -216,6 +630,24 @@ Number of unknown images         =  152
 Number of missclassified images  =  25
 Average computation time of 0.22786
  
+... processing bright attack  with 200 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  16
+Number of not robust images      =  0
+Number of unknown images         =  159
+Number of missclassified images  =  25
+Average computation time of 0.17645
+ 
+... processing bright attack  with 200 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  16
+Number of not robust images      =  0
+Number of unknown images         =  159
+Number of missclassified images  =  25
+Average computation time of 0.23706
+ 
 ... processing bright attack  with 200 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -228,6 +660,24 @@ Average computation time of 0.28619
 *******************************************************
 *******************************************************
 ================= PROCESSING RESULTS: results/verification_multipleAttacks_synapsemnist3d.mat ...
+... processing dark attack  with 50 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  200
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  0
+Average computation time of 0.1092
+ 
+... processing dark attack  with 50 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  200
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  0
+Average computation time of 0.10928
+ 
 ... processing dark attack  with 50 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -237,6 +687,24 @@ Number of unknown images         =  0
 Number of missclassified images  =  0
 Average computation time of 0.10852
  
+... processing dark attack  with 100 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  200
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  0
+Average computation time of 0.10622
+ 
+... processing dark attack  with 100 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  200
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  0
+Average computation time of 0.11437
+ 
 ... processing dark attack  with 100 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -246,6 +714,24 @@ Number of unknown images         =  0
 Number of missclassified images  =  0
 Average computation time of 0.117
  
+... processing dark attack  with 200 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  200
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  0
+Average computation time of 0.10604
+ 
+... processing dark attack  with 200 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  200
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  0
+Average computation time of 0.11188
+ 
 ... processing dark attack  with 200 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -255,6 +741,24 @@ Number of unknown images         =  0
 Number of missclassified images  =  0
 Average computation time of 0.12074
  
+... processing bright attack  with 50 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  200
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  0
+Average computation time of 0.11667
+ 
+... processing bright attack  with 50 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  200
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  0
+Average computation time of 0.12212
+ 
 ... processing bright attack  with 50 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -264,6 +768,24 @@ Number of unknown images         =  0
 Number of missclassified images  =  0
 Average computation time of 0.15097
  
+... processing bright attack  with 100 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  200
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  0
+Average computation time of 0.12318
+ 
+... processing bright attack  with 100 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  200
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  0
+Average computation time of 0.13739
+ 
 ... processing bright attack  with 100 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -273,6 +795,24 @@ Number of unknown images         =  0
 Number of missclassified images  =  0
 Average computation time of 0.15911
  
+... processing bright attack  with 200 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  200
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  0
+Average computation time of 0.14024
+ 
+... processing bright attack  with 200 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  200
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  0
+Average computation time of 0.15435
+ 
 ... processing bright attack  with 200 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -285,6 +825,24 @@ Average computation time of 0.20001
 *******************************************************
 *******************************************************
 ================= PROCESSING RESULTS: results/verification_multipleAttacks_vesselmnist3d.mat ...
+... processing dark attack  with 50 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  150
+Number of not robust images      =  0
+Number of unknown images         =  14
+Number of missclassified images  =  36
+Average computation time of 0.09203
+ 
+... processing dark attack  with 50 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  150
+Number of not robust images      =  0
+Number of unknown images         =  14
+Number of missclassified images  =  36
+Average computation time of 0.10962
+ 
 ... processing dark attack  with 50 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -294,6 +852,24 @@ Number of unknown images         =  14
 Number of missclassified images  =  36
 Average computation time of 0.11734
  
+... processing dark attack  with 100 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  154
+Number of not robust images      =  0
+Number of unknown images         =  10
+Number of missclassified images  =  36
+Average computation time of 0.10295
+ 
+... processing dark attack  with 100 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  154
+Number of not robust images      =  0
+Number of unknown images         =  10
+Number of missclassified images  =  36
+Average computation time of 0.10439
+ 
 ... processing dark attack  with 100 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -303,6 +879,24 @@ Number of unknown images         =  10
 Number of missclassified images  =  36
 Average computation time of 0.11599
  
+... processing dark attack  with 200 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  156
+Number of not robust images      =  0
+Number of unknown images         =  8
+Number of missclassified images  =  36
+Average computation time of 0.10085
+ 
+... processing dark attack  with 200 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  156
+Number of not robust images      =  0
+Number of unknown images         =  8
+Number of missclassified images  =  36
+Average computation time of 0.09907
+ 
 ... processing dark attack  with 200 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -312,6 +906,24 @@ Number of unknown images         =  8
 Number of missclassified images  =  36
 Average computation time of 0.10129
  
+... processing bright attack  with 50 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  164
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  36
+Average computation time of 0.10218
+ 
+... processing bright attack  with 50 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  164
+Number of not robust images      =  0
+Number of unknown images         =  0
+Number of missclassified images  =  36
+Average computation time of 0.11383
+ 
 ... processing bright attack  with 50 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -321,6 +933,24 @@ Number of unknown images         =  0
 Number of missclassified images  =  36
 Average computation time of 0.19815
  
+... processing bright attack  with 100 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  163
+Number of not robust images      =  0
+Number of unknown images         =  1
+Number of missclassified images  =  36
+Average computation time of 0.1618
+ 
+... processing bright attack  with 100 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  163
+Number of not robust images      =  0
+Number of unknown images         =  1
+Number of missclassified images  =  36
+Average computation time of 0.20755
+ 
 ... processing bright attack  with 100 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
@@ -330,6 +960,24 @@ Number of unknown images         =  1
 Number of missclassified images  =  36
 Average computation time of 0.18294
  
+... processing bright attack  with 200 pixels perturbed with noise of 1
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  161
+Number of not robust images      =  0
+Number of unknown images         =  3
+Number of missclassified images  =  36
+Average computation time of 0.14705
+ 
+... processing bright attack  with 200 pixels perturbed with noise of 2
+----------- ROBUSTNESS RESULTS -------------
+Verification results of 200 images.
+Number of robust images          =  161
+Number of not robust images      =  0
+Number of unknown images         =  3
+Number of missclassified images  =  36
+Average computation time of 0.2101
+ 
 ... processing bright attack  with 200 pixels perturbed with noise of 3
 ----------- ROBUSTNESS RESULTS -------------
 Verification results of 200 images.
diff --git a/code/nnv/examples/NN/medmnist/process_results.m b/code/nnv/examples/NN/medmnist/process_results.m
index f5bf64507b..7d80990b79 100644
--- a/code/nnv/examples/NN/medmnist/process_results.m
+++ b/code/nnv/examples/NN/medmnist/process_results.m
@@ -39,7 +39,7 @@ function process_multiple_attacks(results)
     noise_vals = [1;2;3];
     for i = 1:n(3)
         for j = 1:n(4)
-            for k = n(5)
+            for k = 1:n(5)
                 % Print what results we are looking into
                 disp("... processing " + names(i) + " attack  with " ...
                     +string(max_pixels(j)) + " pixels perturbed with noise of "+ string(noise_vals(k)));
diff --git a/code/nnv/examples/Tutorial/other/set_representations.m b/code/nnv/examples/Tutorial/other/set_representations.m
index 78ad4d44d8..08b748fe08 100644
--- a/code/nnv/examples/Tutorial/other/set_representations.m
+++ b/code/nnv/examples/Tutorial/other/set_representations.m
@@ -64,11 +64,12 @@
 
 S1 = S.affineMap([1 0 0 0 0; 0 1 0 0 0], []); % 2D, dims 1 and 2
 figure;
-Star.plot(S1);
+Star.plotBoxes_2D(S1,1,2,'r');
+hold on;
 
 S3 = S.affineMap([1 0 0 0 0; 0 1 0 0 0; 0 0 1 0 0], []); % 3D, dims 1,2 and 3
 figure;
-Star.plot(S3);
+Star.plotBoxes_3D(S3,1,2,3,'r');
 
 % 2) Plot an overapproximation (box around Star) of the set and plot
 
diff --git a/code/nnv/tests/nn/layers/AveragePooling2DLayer/test_layers_averagePooling.m b/code/nnv/tests/nn/layers/AveragePooling2DLayer/test_layers_averagePooling.m
index 71e8b89895..8f4314c194 100644
--- a/code/nnv/tests/nn/layers/AveragePooling2DLayer/test_layers_averagePooling.m
+++ b/code/nnv/tests/nn/layers/AveragePooling2DLayer/test_layers_averagePooling.m
@@ -39,6 +39,15 @@
     % display(inputVol(:,:,i));
 end
 
+%% test 3b: AveragePooling2DLayer evaluation - single precision
+% original input volume: color image with 3 channels
+inputVol(:, :, 1) = [0 0 2 0 0; 1 2 0 2 0; 0 0 2 2 0; 0 2 2 2 2; 2 2 2 1 1]; % channel 1 input matrix
+inputVol(:, :, 2) = [1 2 2 1 2; 2 1 2 0 2; 2 2 2 0 1; 1 1 1 0 0; 1 0 2 2 1]; % channel 2 input matrix
+inputVol(:, :, 3) = [0 0 2 2 1; 0 2 1 1 2; 0 2 0 0 1; 0 2 1 0 1; 1 2 1 0 0]; % channel 3 input matrix
+
+L = AveragePooling2DLayer([3 3], [2 2], [0 0 0 0]);
+y = L.evaluate(single(inputVol));
+
 
 %% test 4: AveragePooling2DLayer get zero padding input
 
diff --git a/code/nnv/tests/nn/layers/Conv2DLayer/test_layers_Conv2D.m b/code/nnv/tests/nn/layers/Conv2DLayer/test_layers_Conv2D.m
index 6e3712613b..3042dc4fb7 100644
--- a/code/nnv/tests/nn/layers/Conv2DLayer/test_layers_Conv2D.m
+++ b/code/nnv/tests/nn/layers/Conv2DLayer/test_layers_Conv2D.m
@@ -10,13 +10,9 @@
 
 
 
-%___________________________________________________________________________________________________
-%tests below originally taken from test_Conv2DLayer_compute_featureMap.m
-
 
 %% test 1: Conv2DLayer feature map
 
-
 I = [1 1 1 0 0; 0 1 1 1 0; 0 0 1 1 1; 0 0 1 1 0; 0 1 1 0 0]; % input
 W = [1 0 1; 0 1 0; 1 0 1]; % filter
 
@@ -24,21 +20,13 @@
 stride = [1 1];
 dilation = [1 1];
 
-
 featureMap = Conv2DLayer.compute_featureMap(I, W, padding, stride, dilation);
 
-
 checker=[I(1, 1)+I(1, 3)+I(2, 2)+I(3, 1)+I(3, 3), I(1, 2)+I(1, 4)+I(2, 3)+I(3, 2)+I(3, 4), I(1, 3)+I(1, 5)+I(2, 4)+I(3, 3)+I(3, 5);  I(2, 1)+I(2, 3)+I(3, 2)+I(4, 1)+I(4, 3), I(2, 2)+I(2, 4)+I(3, 3)+I(4, 2)+I(4, 4), I(2, 3)+I(2, 5)+I(3, 4)+I(4, 3)+I(4, 5); I(3, 1)+I(3, 3)+I(4, 2)+I(5, 1)+I(5, 3), I(3, 2)+I(3, 4)+I(4, 3)+I(5, 2)+I(5, 4), I(3, 3)+I(3, 5)+I(4, 4)+I(5, 3)+I(5, 5);];   
 
 assert(isequal(checker, featureMap))
 
 
-
-
-%___________________________________________________________________________________________________
-%tests below originally taken from test_Conv2DLayer_constructor.m
-
-
 %% test 2: Conv2DLayer constructor
 
 
@@ -50,6 +38,7 @@
 W(:,:, 1, 1) = [1 1 1; 1 1 -1; 0 -1 0]; % channel 1
 W(:, :, 2, 1) = [0 1 1; 0 -1 0; 0 0 -1]; % channel 2
 W(:, :, 3, 1) = [1 -1 0; 1 1 0; -1 0 1]; % channel 3
+
 % filter 2 weight matrix with 3 channels
 W(:, :, 1, 2) = [1 1 -1; -1 0 1; 1 -1 -1]; % channel 1
 W(:, :, 2, 2) = [-1 1 1; 0 1 1; 0 -1 1]; % channel 2
@@ -59,27 +48,17 @@
 b(:, :, 1) = 1; % filter 1
 b(:, :, 2) = 0; % filter 2
 
-
 L0 = Conv2DLayer(W, b);
 
 L1 = Conv2DLayer('Test_Cov2DLayer', W, b, [1 1 1 1], [1 1], [1 1]);
 
-
-
-%___________________________________________________________________________________________________
-%tests below originally taken from test_Conv2DLayer_get_zero_padding_input.m
-
-
 %% test 3: Conv2DLayer zero padding
 
-
-
 % original input volume: color image with 3 channels
 inputVol(:, :, 1) = [2 0 1 2 1; 1 0 2 2 2; 1 2 2 0 2; 1 2 0 0 1; 1 0 1 1 2]; % channel 1 input matrix
 inputVol(:, :, 2) = [0 0 1 0 1; 0 0 2 1 1; 1 1 0 1 1; 1 1 0 2 2; 2 1 2 0 0]; % channel 2 input matrix
 inputVol(:, :, 3) = [1 2 2 1 0; 2 0 0 2 0; 0 0 1 0 1; 1 2 0 2 0; 1 0 2 1 0]; % channel 3 input matrix
 
-
 % construct input with padding operation
 paddingSize = [1 1 1 1];
 I = Conv2DLayer.get_zero_padding_input(inputVol, paddingSize);
@@ -95,11 +74,6 @@
 assert(isempty(find(left_right)))
 
 
-
-%___________________________________________________________________________________________________
-%tests below originally taken from test_Conv2DLayer_reach.m
-
-
 %% test 4: Conv2DLayer reach
 
 % construct a Conv2DLayer object
@@ -110,6 +84,7 @@
 W(:,:, 1, 1) = [1 1 1; 1 1 -1; 0 -1 0]; % channel 1
 W(:, :, 2, 1) = [0 1 1; 0 -1 0; 0 0 -1]; % channel 2
 W(:, :, 3, 1) = [1 -1 0; 1 1 0; -1 0 1]; % channel 3
+
 % filter 2 weight matrix with 3 channels
 W(:, :, 1, 2) = [1 1 -1; -1 0 1; 1 -1 -1]; % channel 1
 W(:, :, 2, 2) = [-1 1 1; 0 1 1; 0 -1 1]; % channel 2
@@ -119,7 +94,6 @@
 b(:, :, 1) = 1; % filter 1
 b(:, :, 2) = 0; % filter 2
 
-
 L0 = Conv2DLayer(W, b);
 
 LB(:,:,1) = [-0.1 -0.2 0 0; 0 0 0 0; 0 0 0 0; 0 0 0 0]; % attack on pixel (1,1) and (1,2)
@@ -130,21 +104,49 @@
 UB(:,:,2) = [0.1 0.15 0 0; 0 0 0 0; 0 0 0 0; 0 0 0 0];
 UB(:,:,3) = UB(:,:,2);
 
-image = ImageZono(LB, UB);
+image = ImageStar(LB, UB);
+
+L0.reach(image);
+
+%% %% test 4b: Conv2DLayer reach (single precision)
 
-%Z = L0.reach(image); %THIS TRIGGERS AN ERROR, BECAUSE IT'S NOT AN IMAGE
+% construct a Conv2DLayer object
+% this convolution layer is from the link: 
+% http://cs231n.github.io/convolutional-networks/
+
+% filter 1 weight matrix with 3 channels
+W(:,:, 1, 1) = [1 1 1; 1 1 -1; 0 -1 0]; % channel 1
+W(:, :, 2, 1) = [0 1 1; 0 -1 0; 0 0 -1]; % channel 2
+W(:, :, 3, 1) = [1 -1 0; 1 1 0; -1 0 1]; % channel 3
+
+% filter 2 weight matrix with 3 channels
+W(:, :, 1, 2) = [1 1 -1; -1 0 1; 1 -1 -1]; % channel 1
+W(:, :, 2, 2) = [-1 1 1; 0 1 1; 0 -1 1]; % channel 2
+W(:, :, 3, 2) = [-1 1 -1; -1 0 -1; -1 -1 -1]; % channel 3
+
+% biases for 2 filters
+b(:, :, 1) = 1; % filter 1
+b(:, :, 2) = 0; % filter 2
 
-S = L0.reach(image.toImageStar);
+L0 = Conv2DLayer(single(W), single(b));
 
+LB(:,:,1) = [-0.1 -0.2 0 0; 0 0 0 0; 0 0 0 0; 0 0 0 0]; % attack on pixel (1,1) and (1,2)
+LB(:,:,2) = [-0.1 -0.15 0 0; 0 0 0 0; 0 0 0 0; 0 0 0 0]; 
+LB(:,:,3) = LB(:,:,2);
 
+UB(:,:,1) = [0 0.2 0 0; 0 0 0 0; 0 0 0 0; 0 0 0 0];
+UB(:,:,2) = [0.1 0.15 0 0; 0 0 0 0; 0 0 0 0; 0 0 0 0];
+UB(:,:,3) = UB(:,:,2);
 
-%___________________________________________________________________________________________________
-%tests below originally taken from test_Conv2DLayer_reach_star_exact_single_input.m
+image = ImageStar(LB, UB);
 
+image = image.changeVarsPrecision('single');
 
-%% test 5: Conv2DLayer reach star exact
+L0.reach(image);
 
 
+%% test 5: Conv2DLayer reach star exact
+
 % construct a Conv2DLayer object
 % this convolution layer is from the link: 
 % http://cs231n.github.io/convolutional-networks/
@@ -164,12 +166,8 @@
 b(:, :, 1) = 1; % filter 1
 b(:, :, 2) = 0; % filter 2
 
-
-
-
 L = Conv2DLayer(W, b);
-L.set_weights_biases(W, b);
-
+% L.set_weights_biases(W, b);
 
 % the convolutional layer has 2 filters of size 3x3
 padding = 1;
@@ -185,7 +183,6 @@
 IM(:, :, 2) = [1 2 2 1 2; 2 1 2 0 2; 2 2 2 0 1; 1 1 1 0 0; 1 0 2 2 1]; % channel 2 input matrix
 IM(:, :, 3) = [0 0 2 2 1; 0 2 1 1 2; 0 2 0 0 1; 0 2 1 0 1; 1 2 1 0 0]; % channel 3 input matrix
 
-
 LB(:,:,1) = [-0.1 -0.2 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0]; % attack on pixel (1,1) and (1,2)
 LB(:,:,2) = [-0.1 -0.15 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0]; 
 LB(:,:,3) = LB(:,:,2);
@@ -196,24 +193,55 @@
 
 input = ImageStar(IM, LB, UB);
 
-
 Y = L.reach_star_single_input(input);
 
+%% test 5b: Conv2DLayer reach star exact (single precision input)
 
+% construct a Conv2DLayer object
+% this convolution layer is from the link: 
+% http://cs231n.github.io/convolutional-networks/
 
+% filter has a size of 3 x 3
+% filter 1 weight matrix with 3 channels
 
+W(:,:, 1, 1) = [-1 0 0; 1 1 0; -1 0 -1]; % channel 1
+W(:, :, 2, 1) = [-1 0 -1; 1 -1 1; 1 1 0]; % channel 2
+W(:, :, 3, 1) = [-1 1 1; -1 -1 -1; 0 0 -1]; % channel 3
+% filter 2 weight matrix with 3 channels
+W(:, :, 1, 2) = [-1 1 0; 0 0 -1; 1 0 0]; % channel 1
+W(:, :, 2, 2) = [1 0 0; 0 0 -1; 0 0 1]; % channel 2
+W(:, :, 3, 2) = [1 -1 -1; 1 1 0; -1 0 1]; % channel 3
 
+% biases for 2 filters
+b(:, :, 1) = 1; % filter 1
+b(:, :, 2) = 0; % filter 2
 
+L = Conv2DLayer(single(W), single(b));
+% L.set_weights_biases(W, b);
 
+% the convolutional layer has 2 filters of size 3x3
+padding = 1;
+stride = 2;
+dilation = 1;
 
+L.set_stride(stride);
+L.set_padding(padding);
+L.set_dilation(dilation);
 
+% original input volume: color image with 3 channels
+IM(:, :, 1) = [0 0 2 0 0; 1 2 0 2 0; 0 0 2 2 0; 0 2 2 2 2; 2 2 2 1 1]; % channel 1 input matrix
+IM(:, :, 2) = [1 2 2 1 2; 2 1 2 0 2; 2 2 2 0 1; 1 1 1 0 0; 1 0 2 2 1]; % channel 2 input matrix
+IM(:, :, 3) = [0 0 2 2 1; 0 2 1 1 2; 0 2 0 0 1; 0 2 1 0 1; 1 2 1 0 0]; % channel 3 input matrix
 
+LB(:,:,1) = [-0.1 -0.2 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0]; % attack on pixel (1,1) and (1,2)
+LB(:,:,2) = [-0.1 -0.15 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0]; 
+LB(:,:,3) = LB(:,:,2);
 
+UB(:,:,1) = [0.1 0.2 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0;0 0 0 0 0];
+UB(:,:,2) = [0.1 0.15 0 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0;0 0 0 0 0];
+UB(:,:,3) = UB(:,:,2);
 
+input = ImageStar(IM, LB, UB);
+input = input.changeVarsPrecision('single');
 
-
-
-
-
-
-
+Y = L.reach_star_single_input(input);
\ No newline at end of file
diff --git a/code/nnv/tests/set/star/test_star_getZono.m b/code/nnv/tests/set/star/test_star_getZono.m
index 179ed60855..e309740f0a 100644
--- a/code/nnv/tests/set/star/test_star_getZono.m
+++ b/code/nnv/tests/set/star/test_star_getZono.m
@@ -1,3 +1,4 @@
+rng(3);
 center = [1; 1];
 V = [1 0 1; 0 1 1];
 P = ExamplePoly.randHrep('d',3);
diff --git a/code/nnv/tests/set/star/test_star_plotBoxes_3D.m b/code/nnv/tests/set/star/test_star_plotBoxes_3D.m
index b6d08f9b7e..80d873f943 100644
--- a/code/nnv/tests/set/star/test_star_plotBoxes_3D.m
+++ b/code/nnv/tests/set/star/test_star_plotBoxes_3D.m
@@ -1,3 +1,4 @@
+rng(1);
 center = [1; 1; 1];
 V = [1 0 1; 0 1 1; 1 0 0];
 P = ExamplePoly.randHrep('d',3);