diff --git a/applications/Shepard_CNN/Shepard_super_res/shepard_sr_x2_train.m b/applications/Shepard_CNN/Shepard_super_res/shepard_sr_x2_train.m
index 2375929..63201fe 100644
--- a/applications/Shepard_CNN/Shepard_super_res/shepard_sr_x2_train.m
+++ b/applications/Shepard_CNN/Shepard_super_res/shepard_sr_x2_train.m
@@ -1,114 +1,114 @@
-%--------------------------------------------------------------------------------------------------------
-% The system is created based on the principles described in the following paper
-% Jimmy SJ. Ren, Li Xu, Qiong Yan, Wenxiu Sun, "Shepard Convolutional Neural Networks", 
-% Advances in Neural Information Processing Systems (NIPS 2015)
-% email: jimmy.sj.ren@gmail.com
-%--------------------------------------------------------------------------------------------------------
-addpath applications/Shepard_CNN/Shepard_super_res/
-addpath utils/
-addpath cuda/
-addpath mem/
-addpath layers/
-addpath layers_adapters/
-addpath optimization/
-addpath pipeline/
-addpath data/
-
-clearvars -global config;
-clearvars -global mem;
-clear gen_mask_patch_cat_idx_for_super_res;
-clear;
-global config mem;
-shepard_sr_x2_configure();
-init(0);
-
-load('data/Shepard_CNN/Shepard_super_res/x2/val_1ch/val_1');
-
-%images_t = reshape(images_t, size(images_t,1), size(images_t,2), 1, size(images_t,3));
-%labels_t = reshape(labels_t, size(labels_t,1), size(labels_t,2), 1, size(labels_t,3));
-
-perm = randperm(size(images_t, 4));
-images_t = images_t(:,:,:,perm);
-labels_t = labels_t(:,:,:,perm);
-test_samples = config.NEW_MEM(images_t(:,:,:,1:1000));
-test_labels = config.NEW_MEM(labels_t(:,:,:,1:1000));
-
-mask = config.NEW_MEM([1 0; 0 0]);
-mask = repmat(mask, config.input_size(1)/2, config.input_size(2)/2, config.chs);
-mask = repmat(mask, 1,1,1,config.batch_size);
-
-count = 0;
-cost_avg = 0;
-epoc = 0;
-points_seen = 0;
-display_points = 5000;
-save_points = 50000;
-max_grad = 1;
-fprintf('%s\n', datestr(now, 'dd-mm-yyyy HH:MM:SS FFF'));
-for pass = 1:10
-    for p = 1:50
-        load(strcat('data/Shepard_CNN/Shepard_super_res/x2/train_1ch/patches_', num2str(p), '.mat'));
-        
-        %images = reshape(images, size(images,1), size(images,2), 1, size(images,3));
-        %labels = reshape(labels, size(labels,1), size(labels,2), 1, size(labels,3));
-        
-        perm = randperm(20000);
-        images = images(:,:,:,perm);
-        labels = labels(:,:,:,perm);
-        train_imgs = config.NEW_MEM(images);
-        train_labels = config.NEW_MEM(labels);
-        
-        for i = 1:size(train_labels, 4) / config.batch_size            
-            points_seen = points_seen + config.batch_size;
-            in = train_imgs(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
-            %in = train_labels(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
-            out = train_labels(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
-            out = out((size(in, 1) - config.output_size(1)) / 2 + 1:(size(in, 1) - config.output_size(1)) / 2 + config.output_size(1), ...
-                      (size(in, 2) - config.output_size(2)) / 2 + 1:(size(in, 2) - config.output_size(2)) / 2 + config.output_size(2), :, :);
-            
-                  
-            % make the mask list
-            mask_li = {};
-            mask_li{1} = mask;
-            % operate the training pipeline
-            op_train_pipe_with_mask(in.*mask, mask_li, out);
-            % update the weights
-            config.UPDATE_WEIGHTS();
-            
-            if(cost_avg == 0)
-                cost_avg = config.cost;
-            else
-                cost_avg = (cost_avg + config.cost) / 2;
-            end
-
-            % display point
-            if(mod(points_seen, display_points) == 0)
-                count = count + 1;
-                fprintf('%d ', count);
-            end
-            % save point
-            if(mod(points_seen, save_points) == 0)
-                fprintf('\n%s', datestr(now, 'dd-mm-yyyy HH:MM:SS FFF'));
-                epoc = epoc + 1;
-                test_cost = 0;
-                for t = 1:size(test_samples, 4) / config.batch_size
-                    t_label = test_labels(:,:,:,(t-1)*config.batch_size+1:t*config.batch_size);
-                    t_label = config.NEW_MEM(t_label((size(in, 1) - config.output_size(1)) / 2 + 1:(size(in, 1) - config.output_size(1)) / 2 + config.output_size(1), ...
-                                            (size(in, 2) - config.output_size(2)) / 2 + 1:(size(in, 2) - config.output_size(2)) / 2 + config.output_size(2), :));
-                    
-                    op_test_pipe_with_mask(test_samples(:,:,:,(t-1)*config.batch_size+1:t*config.batch_size).*mask, mask_li, t_label);
-                    
-                    test_out = gather(mem.output);
-                    test_cost = test_cost + config.cost;
-                end
-                test_cost = test_cost / (size(test_samples, 4) / config.batch_size);
-                fprintf('\nepoc %d, training avg cost: %f, test avg cost: %f\n', epoc, cost_avg, test_cost);                
-                
-                save_weights(strcat('applications/Shepard_CNN/Shepard_super_res/results/shepard_layer_x2/epoc', num2str(epoc), '.mat'));
-                
-                cost_avg = 0;
-            end
-        end
-    end
-end
-
+%--------------------------------------------------------------------------------------------------------
+% The system is created based on the principles described in the following paper
+% Jimmy SJ. Ren, Li Xu, Qiong Yan, Wenxiu Sun, "Shepard Convolutional Neural Networks", 
+% Advances in Neural Information Processing Systems (NIPS 2015)
+% email: jimmy.sj.ren@gmail.com
+%--------------------------------------------------------------------------------------------------------
+addpath applications/Shepard_CNN/Shepard_super_res/
+addpath utils/
+addpath cuda/
+addpath mem/
+addpath layers/
+addpath layers_adapters/
+addpath optimization/
+addpath pipeline/
+addpath data/
+
+clearvars -global config;
+clearvars -global mem;
+clear gen_mask_patch_cat_idx_for_super_res;
+clear;
+global config mem;
+shepard_sr_x2_configure();
+init(0);
+
+load('data/Shepard_CNN/Shepard_super_res/x2/val_1ch/val_1');
+
+images_t = reshape(images_t, size(images_t,1), size(images_t,2), 1, size(images_t,3));
+labels_t = reshape(labels_t, size(labels_t,1), size(labels_t,2), 1, size(labels_t,3));
+
+perm = randperm(size(images_t, 4));
+images_t = images_t(:,:,:,perm);
+labels_t = labels_t(:,:,:,perm);
+test_samples = config.NEW_MEM(images_t(:,:,:,1:1000));
+test_labels = config.NEW_MEM(labels_t(:,:,:,1:1000));
+
+mask = config.NEW_MEM([1 0; 0 0]);
+mask = repmat(mask, config.input_size(1)/2, config.input_size(2)/2, config.chs);
+mask = repmat(mask, 1,1,1,config.batch_size);
+
+count = 0;
+cost_avg = 0;
+epoc = 0;
+points_seen = 0;
+display_points = 5000;
+save_points = 50000;
+max_grad = 1;
+fprintf('%s\n', datestr(now, 'dd-mm-yyyy HH:MM:SS FFF'));
+for pass = 1:10
+    for p = 1:50
+        load(strcat('data/Shepard_CNN/Shepard_super_res/x2/train_1ch/patches_', num2str(p), '.mat'));
+        
+        images = reshape(images, size(images,1), size(images,2), 1, size(images,3));
+        labels = reshape(labels, size(labels,1), size(labels,2), 1, size(labels,3));
+        
+        perm = randperm(20000);
+        images = images(:,:,:,perm);
+        labels = labels(:,:,:,perm);
+        train_imgs = config.NEW_MEM(images);
+        train_labels = config.NEW_MEM(labels);
+        
+        for i = 1:size(train_labels, 4) / config.batch_size            
+            points_seen = points_seen + config.batch_size;
+            in = train_imgs(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
+            %in = train_labels(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
+            out = train_labels(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
+            out = out((size(in, 1) - config.output_size(1)) / 2 + 1:(size(in, 1) - config.output_size(1)) / 2 + config.output_size(1), ...
+                      (size(in, 2) - config.output_size(2)) / 2 + 1:(size(in, 2) - config.output_size(2)) / 2 + config.output_size(2), :, :);
+            
+                  
+            % make the mask list
+            mask_li = {};
+            mask_li{1} = mask;
+            % operate the training pipeline
+            op_train_pipe_with_mask(in.*mask, mask_li, out);
+            % update the weights
+            config.UPDATE_WEIGHTS();
+            
+            if(cost_avg == 0)
+                cost_avg = config.cost;
+            else
+                cost_avg = (cost_avg + config.cost) / 2;
+            end
+
+            % display point
+            if(mod(points_seen, display_points) == 0)
+                count = count + 1;
+                fprintf('%d ', count);
+            end
+            % save point
+            if(mod(points_seen, save_points) == 0)
+                fprintf('\n%s', datestr(now, 'dd-mm-yyyy HH:MM:SS FFF'));
+                epoc = epoc + 1;
+                test_cost = 0;
+                for t = 1:size(test_samples, 4) / config.batch_size
+                    t_label = test_labels(:,:,:,(t-1)*config.batch_size+1:t*config.batch_size);
+                    t_label = config.NEW_MEM(t_label((size(in, 1) - config.output_size(1)) / 2 + 1:(size(in, 1) - config.output_size(1)) / 2 + config.output_size(1), ...
+                                            (size(in, 2) - config.output_size(2)) / 2 + 1:(size(in, 2) - config.output_size(2)) / 2 + config.output_size(2), :));
+                    
+                    op_test_pipe_with_mask(test_samples(:,:,:,(t-1)*config.batch_size+1:t*config.batch_size).*mask, mask_li, t_label);
+                    
+                    test_out = gather(mem.output);
+                    test_cost = test_cost + config.cost;
+                end
+                test_cost = test_cost / (size(test_samples, 4) / config.batch_size);
+                fprintf('\nepoc %d, training avg cost: %f, test avg cost: %f\n', epoc, cost_avg, test_cost);                
+                
+                save_weights(strcat('applications/Shepard_CNN/Shepard_super_res/results/shepard_layer_x2/epoc', num2str(epoc), '.mat'));
+                
+                cost_avg = 0;
+            end
+        end
+    end
+end
+
diff --git a/applications/Shepard_CNN/Shepard_super_res/shepard_sr_x3_train.m b/applications/Shepard_CNN/Shepard_super_res/shepard_sr_x3_train.m
index 75e9074..539c03b 100644
--- a/applications/Shepard_CNN/Shepard_super_res/shepard_sr_x3_train.m
+++ b/applications/Shepard_CNN/Shepard_super_res/shepard_sr_x3_train.m
@@ -1,114 +1,114 @@
-%--------------------------------------------------------------------------------------------------------
-% The system is created based on the principles described in the following paper
-% Jimmy SJ. Ren, Li Xu, Qiong Yan, Wenxiu Sun, "Shepard Convolutional Neural Networks", 
-% Advances in Neural Information Processing Systems (NIPS 2015)
-% email: jimmy.sj.ren@gmail.com
-%--------------------------------------------------------------------------------------------------------
-addpath applications/Shepard_CNN/Shepard_super_res/
-addpath utils/
-addpath cuda/
-addpath mem/
-addpath layers/
-addpath layers_adapters/
-addpath optimization/
-addpath pipeline/
-addpath data/
-
-clearvars -global config;
-clearvars -global mem;
-clear gen_mask_patch_cat_idx_for_super_res;
-clear;
-global config mem;
-shepard_sr_x3_configure();
-init(0);
-
-load('data/Shepard_CNN/Shepard_super_res/x3/val_1ch/val_1');
-
-%images_t = reshape(images_t, size(images_t,1), size(images_t,2), 1, size(images_t,3));
-%labels_t = reshape(labels_t, size(labels_t,1), size(labels_t,2), 1, size(labels_t,3));
-
-perm = randperm(size(images_t, 4));
-images_t = images_t(:,:,:,perm);
-labels_t = labels_t(:,:,:,perm);
-test_samples = config.NEW_MEM(images_t(:,:,:,1:1000));
-test_labels = config.NEW_MEM(labels_t(:,:,:,1:1000));
-
-mask = config.NEW_MEM([1 0 0; 0 0 0; 0 0 0]);
-mask = repmat(mask, config.input_size(1)/3, config.input_size(2)/3, config.chs);
-mask = repmat(mask, 1,1,1,config.batch_size);
-
-count = 0;
-cost_avg = 0;
-epoc = 0;
-points_seen = 0;
-display_points = 5000;
-save_points = 50000;
-max_grad = 1;
-fprintf('%s\n', datestr(now, 'dd-mm-yyyy HH:MM:SS FFF'));
-for pass = 1:50
-    for p = 1:50
-        load(strcat('data/Shepard_CNN/Shepard_super_res/x3/train_1ch/patches_', num2str(p), '.mat'));
-        
-        %images = reshape(images, size(images,1), size(images,2), 1, size(images,3));
-        %labels = reshape(labels, size(labels,1), size(labels,2), 1, size(labels,3));
-        
-        perm = randperm(20000);
-        images = images(:,:,:,perm);
-        labels = labels(:,:,:,perm);
-        train_imgs = config.NEW_MEM(images);
-        train_labels = config.NEW_MEM(labels);
-        
-        for i = 1:size(train_labels, 4) / config.batch_size            
-            points_seen = points_seen + config.batch_size;
-            in = train_imgs(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
-            %in = train_labels(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
-            out = train_labels(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
-            out = out((size(in, 1) - config.output_size(1)) / 2 + 1:(size(in, 1) - config.output_size(1)) / 2 + config.output_size(1), ...
-                      (size(in, 2) - config.output_size(2)) / 2 + 1:(size(in, 2) - config.output_size(2)) / 2 + config.output_size(2), :, :);
-            
-                  
-            % make the mask list
-            mask_li = {};
-            mask_li{1} = mask;
-            % operate the training pipeline
-            op_train_pipe_with_mask(in.*mask, mask_li, out);
-            % update the weights
-            config.UPDATE_WEIGHTS();
-            
-            if(cost_avg == 0)
-                cost_avg = config.cost;
-            else
-                cost_avg = (cost_avg + config.cost) / 2;
-            end
-
-            % display point
-            if(mod(points_seen, display_points) == 0)
-                count = count + 1;
-                fprintf('%d ', count);
-            end
-            % save point
-            if(mod(points_seen, save_points) == 0)
-                fprintf('\n%s', datestr(now, 'dd-mm-yyyy HH:MM:SS FFF'));
-                epoc = epoc + 1;
-                test_cost = 0;
-                for t = 1:size(test_samples, 4) / config.batch_size
-                    t_label = test_labels(:,:,:,(t-1)*config.batch_size+1:t*config.batch_size);
-                    t_label = config.NEW_MEM(t_label((size(in, 1) - config.output_size(1)) / 2 + 1:(size(in, 1) - config.output_size(1)) / 2 + config.output_size(1), ...
-                                            (size(in, 2) - config.output_size(2)) / 2 + 1:(size(in, 2) - config.output_size(2)) / 2 + config.output_size(2), :));
-                    
-                    op_test_pipe_with_mask(test_samples(:,:,:,(t-1)*config.batch_size+1:t*config.batch_size).*mask, mask_li, t_label);
-                    
-                    test_out = gather(mem.output);
-                    test_cost = test_cost + config.cost;
-                end
-                test_cost = test_cost / (size(test_samples, 4) / config.batch_size);
-                fprintf('\nepoc %d, training avg cost: %f, test avg cost: %f\n', epoc, cost_avg, test_cost);                
-                
-                save_weights(strcat('applications/Shepard_CNN/Shepard_super_res/results/shepard_layer_x3/epoc', num2str(epoc), '.mat'));
-                
-                cost_avg = 0;
-            end
-        end
-    end
-end
-
+%--------------------------------------------------------------------------------------------------------
+% The system is created based on the principles described in the following paper
+% Jimmy SJ. Ren, Li Xu, Qiong Yan, Wenxiu Sun, "Shepard Convolutional Neural Networks", 
+% Advances in Neural Information Processing Systems (NIPS 2015)
+% email: jimmy.sj.ren@gmail.com
+%--------------------------------------------------------------------------------------------------------
+addpath applications/Shepard_CNN/Shepard_super_res/
+addpath utils/
+addpath cuda/
+addpath mem/
+addpath layers/
+addpath layers_adapters/
+addpath optimization/
+addpath pipeline/
+addpath data/
+
+clearvars -global config;
+clearvars -global mem;
+clear gen_mask_patch_cat_idx_for_super_res;
+clear;
+global config mem;
+shepard_sr_x3_configure();
+init(0);
+
+load('data/Shepard_CNN/Shepard_super_res/x3/val_1ch/val_1');
+
+images_t = reshape(images_t, size(images_t,1), size(images_t,2), 1, size(images_t,3));
+labels_t = reshape(labels_t, size(labels_t,1), size(labels_t,2), 1, size(labels_t,3));
+
+perm = randperm(size(images_t, 4));
+images_t = images_t(:,:,:,perm);
+labels_t = labels_t(:,:,:,perm);
+test_samples = config.NEW_MEM(images_t(:,:,:,1:1000));
+test_labels = config.NEW_MEM(labels_t(:,:,:,1:1000));
+
+mask = config.NEW_MEM([1 0 0; 0 0 0; 0 0 0]);
+mask = repmat(mask, config.input_size(1)/3, config.input_size(2)/3, config.chs);
+mask = repmat(mask, 1,1,1,config.batch_size);
+
+count = 0;
+cost_avg = 0;
+epoc = 0;
+points_seen = 0;
+display_points = 5000;
+save_points = 50000;
+max_grad = 1;
+fprintf('%s\n', datestr(now, 'dd-mm-yyyy HH:MM:SS FFF'));
+for pass = 1:50
+    for p = 1:50
+        load(strcat('data/Shepard_CNN/Shepard_super_res/x3/train_1ch/patches_', num2str(p), '.mat'));
+        
+        images = reshape(images, size(images,1), size(images,2), 1, size(images,3));
+        labels = reshape(labels, size(labels,1), size(labels,2), 1, size(labels,3));
+        
+        perm = randperm(20000);
+        images = images(:,:,:,perm);
+        labels = labels(:,:,:,perm);
+        train_imgs = config.NEW_MEM(images);
+        train_labels = config.NEW_MEM(labels);
+        
+        for i = 1:size(train_labels, 4) / config.batch_size            
+            points_seen = points_seen + config.batch_size;
+            in = train_imgs(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
+            %in = train_labels(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
+            out = train_labels(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
+            out = out((size(in, 1) - config.output_size(1)) / 2 + 1:(size(in, 1) - config.output_size(1)) / 2 + config.output_size(1), ...
+                      (size(in, 2) - config.output_size(2)) / 2 + 1:(size(in, 2) - config.output_size(2)) / 2 + config.output_size(2), :, :);
+            
+                  
+            % make the mask list
+            mask_li = {};
+            mask_li{1} = mask;
+            % operate the training pipeline
+            op_train_pipe_with_mask(in.*mask, mask_li, out);
+            % update the weights
+            config.UPDATE_WEIGHTS();
+            
+            if(cost_avg == 0)
+                cost_avg = config.cost;
+            else
+                cost_avg = (cost_avg + config.cost) / 2;
+            end
+
+            % display point
+            if(mod(points_seen, display_points) == 0)
+                count = count + 1;
+                fprintf('%d ', count);
+            end
+            % save point
+            if(mod(points_seen, save_points) == 0)
+                fprintf('\n%s', datestr(now, 'dd-mm-yyyy HH:MM:SS FFF'));
+                epoc = epoc + 1;
+                test_cost = 0;
+                for t = 1:size(test_samples, 4) / config.batch_size
+                    t_label = test_labels(:,:,:,(t-1)*config.batch_size+1:t*config.batch_size);
+                    t_label = config.NEW_MEM(t_label((size(in, 1) - config.output_size(1)) / 2 + 1:(size(in, 1) - config.output_size(1)) / 2 + config.output_size(1), ...
+                                            (size(in, 2) - config.output_size(2)) / 2 + 1:(size(in, 2) - config.output_size(2)) / 2 + config.output_size(2), :));
+                    
+                    op_test_pipe_with_mask(test_samples(:,:,:,(t-1)*config.batch_size+1:t*config.batch_size).*mask, mask_li, t_label);
+                    
+                    test_out = gather(mem.output);
+                    test_cost = test_cost + config.cost;
+                end
+                test_cost = test_cost / (size(test_samples, 4) / config.batch_size);
+                fprintf('\nepoc %d, training avg cost: %f, test avg cost: %f\n', epoc, cost_avg, test_cost);                
+                
+                save_weights(strcat('applications/Shepard_CNN/Shepard_super_res/results/shepard_layer_x3/epoc', num2str(epoc), '.mat'));
+                
+                cost_avg = 0;
+            end
+        end
+    end
+end
+
diff --git a/applications/Shepard_CNN/Shepard_super_res/shepard_sr_x4_train.m b/applications/Shepard_CNN/Shepard_super_res/shepard_sr_x4_train.m
index 7da1fb6..14afa7f 100644
--- a/applications/Shepard_CNN/Shepard_super_res/shepard_sr_x4_train.m
+++ b/applications/Shepard_CNN/Shepard_super_res/shepard_sr_x4_train.m
@@ -1,116 +1,116 @@
-%--------------------------------------------------------------------------------------------------------
-% The system is created based on the principles described in the following paper
-% Jimmy SJ. Ren, Li Xu, Qiong Yan, Wenxiu Sun, "Shepard Convolutional Neural Networks", 
-% Advances in Neural Information Processing Systems (NIPS 2015)
-% email: jimmy.sj.ren@gmail.com
-%--------------------------------------------------------------------------------------------------------
-addpath applications/Shepard_CNN/Shepard_super_res/
-addpath utils/
-addpath cuda/
-addpath mem/
-addpath layers/
-addpath layers_adapters/
-addpath optimization/
-addpath pipeline/
-addpath data/
-
-clearvars -global config;
-clearvars -global mem;
-clear gen_mask_patch_cat_idx_for_super_res;
-clear;
-global config mem;
-shepard_sr_x4_configure();
-init(0);
-
-load('data/Shepard_CNN/Shepard_super_res/x4/val_1ch/val_1');
-
-%images_t = reshape(images_t, size(images_t,1), size(images_t,2), 1, size(images_t,3));
-%labels_t = reshape(labels_t, size(labels_t,1), size(labels_t,2), 1, size(labels_t,3));
-
-perm = randperm(size(images_t, 4));
-images_t = images_t(:,:,:,perm);
-labels_t = labels_t(:,:,:,perm);
-test_samples = config.NEW_MEM(images_t(:,:,:,1:1000));
-test_labels = config.NEW_MEM(labels_t(:,:,:,1:1000));
-
-mask = config.NEW_MEM([1 0 0 0;0 0 0 0;0 0 0 0;0 0 0 0]);
-mask = repmat(mask, config.input_size(1)/4, config.input_size(2)/4, config.chs);
-%mask = config.NEW_MEM([1 0; 0 0]);
-%mask = repmat(mask, config.input_size(1)/2, config.input_size(2)/2, config.chs);
-mask = repmat(mask, 1,1,1,config.batch_size);
-
-count = 0;
-cost_avg = 0;
-epoc = 0;
-points_seen = 0;
-display_points = 5000;
-save_points = 50000;
-max_grad = 1;
-fprintf('%s\n', datestr(now, 'dd-mm-yyyy HH:MM:SS FFF'));
-for pass = 1:10
-    for p = 1:50
-        load(strcat('data/Shepard_CNN/Shepard_super_res/x4/train_1ch/patches_', num2str(p), '.mat'));
-        
-        %images = reshape(images, size(images,1), size(images,2), 1, size(images,3));
-        %labels = reshape(labels, size(labels,1), size(labels,2), 1, size(labels,3));
-        
-        perm = randperm(20000);
-        images = images(:,:,:,perm);
-        labels = labels(:,:,:,perm);
-        train_imgs = config.NEW_MEM(images);
-        train_labels = config.NEW_MEM(labels);
-        
-        for i = 1:size(train_labels, 4) / config.batch_size            
-            points_seen = points_seen + config.batch_size;
-            in = train_imgs(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
-            %in = train_labels(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
-            out = train_labels(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
-            out = out((size(in, 1) - config.output_size(1)) / 2 + 1:(size(in, 1) - config.output_size(1)) / 2 + config.output_size(1), ...
-                      (size(in, 2) - config.output_size(2)) / 2 + 1:(size(in, 2) - config.output_size(2)) / 2 + config.output_size(2), :, :);
-            
-                  
-            % make the mask list
-            mask_li = {};
-            mask_li{1} = mask;
-            % operate the training pipeline
-            op_train_pipe_with_mask(in.*mask, mask_li, out);
-            % update the weights
-            config.UPDATE_WEIGHTS();
-            
-            if(cost_avg == 0)
-                cost_avg = config.cost;
-            else
-                cost_avg = (cost_avg + config.cost) / 2;
-            end
-
-            % display point
-            if(mod(points_seen, display_points) == 0)
-                count = count + 1;
-                fprintf('%d ', count);
-            end
-            % save point
-            if(mod(points_seen, save_points) == 0)
-                fprintf('\n%s', datestr(now, 'dd-mm-yyyy HH:MM:SS FFF'));
-                epoc = epoc + 1;
-                test_cost = 0;
-                for t = 1:size(test_samples, 4) / config.batch_size
-                    t_label = test_labels(:,:,:,(t-1)*config.batch_size+1:t*config.batch_size);
-                    t_label = config.NEW_MEM(t_label((size(in, 1) - config.output_size(1)) / 2 + 1:(size(in, 1) - config.output_size(1)) / 2 + config.output_size(1), ...
-                                            (size(in, 2) - config.output_size(2)) / 2 + 1:(size(in, 2) - config.output_size(2)) / 2 + config.output_size(2), :));
-                    
-                    op_test_pipe_with_mask(test_samples(:,:,:,(t-1)*config.batch_size+1:t*config.batch_size).*mask, mask_li, t_label);
-                    
-                    test_out = gather(mem.output);
-                    test_cost = test_cost + config.cost;
-                end
-                test_cost = test_cost / (size(test_samples, 4) / config.batch_size);
-                fprintf('\nepoc %d, training avg cost: %f, test avg cost: %f\n', epoc, cost_avg, test_cost);                
-                
-                save_weights(strcat('applications/Shepard_CNN/Shepard_super_res/results/shepard_layer_x4/epoc', num2str(epoc), '.mat'));
-                
-                cost_avg = 0;
-            end
-        end
-    end
-end
-
+%--------------------------------------------------------------------------------------------------------
+% The system is created based on the principles described in the following paper
+% Jimmy SJ. Ren, Li Xu, Qiong Yan, Wenxiu Sun, "Shepard Convolutional Neural Networks", 
+% Advances in Neural Information Processing Systems (NIPS 2015)
+% email: jimmy.sj.ren@gmail.com
+%--------------------------------------------------------------------------------------------------------
+addpath applications/Shepard_CNN/Shepard_super_res/
+addpath utils/
+addpath cuda/
+addpath mem/
+addpath layers/
+addpath layers_adapters/
+addpath optimization/
+addpath pipeline/
+addpath data/
+
+clearvars -global config;
+clearvars -global mem;
+clear gen_mask_patch_cat_idx_for_super_res;
+clear;
+global config mem;
+shepard_sr_x4_configure();
+init(0);
+
+load('data/Shepard_CNN/Shepard_super_res/x4/val_1ch/val_1');
+
+images_t = reshape(images_t, size(images_t,1), size(images_t,2), 1, size(images_t,3));
+labels_t = reshape(labels_t, size(labels_t,1), size(labels_t,2), 1, size(labels_t,3));
+
+perm = randperm(size(images_t, 4));
+images_t = images_t(:,:,:,perm);
+labels_t = labels_t(:,:,:,perm);
+test_samples = config.NEW_MEM(images_t(:,:,:,1:1000));
+test_labels = config.NEW_MEM(labels_t(:,:,:,1:1000));
+
+mask = config.NEW_MEM([1 0 0 0;0 0 0 0;0 0 0 0;0 0 0 0]);
+mask = repmat(mask, config.input_size(1)/4, config.input_size(2)/4, config.chs);
+%mask = config.NEW_MEM([1 0; 0 0]);
+%mask = repmat(mask, config.input_size(1)/2, config.input_size(2)/2, config.chs);
+mask = repmat(mask, 1,1,1,config.batch_size);
+
+count = 0;
+cost_avg = 0;
+epoc = 0;
+points_seen = 0;
+display_points = 5000;
+save_points = 50000;
+max_grad = 1;
+fprintf('%s\n', datestr(now, 'dd-mm-yyyy HH:MM:SS FFF'));
+for pass = 1:10
+    for p = 1:50
+        load(strcat('data/Shepard_CNN/Shepard_super_res/x4/train_1ch/patches_', num2str(p), '.mat'));
+        
+        images = reshape(images, size(images,1), size(images,2), 1, size(images,3));
+        labels = reshape(labels, size(labels,1), size(labels,2), 1, size(labels,3));
+        
+        perm = randperm(20000);
+        images = images(:,:,:,perm);
+        labels = labels(:,:,:,perm);
+        train_imgs = config.NEW_MEM(images);
+        train_labels = config.NEW_MEM(labels);
+        
+        for i = 1:size(train_labels, 4) / config.batch_size            
+            points_seen = points_seen + config.batch_size;
+            in = train_imgs(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
+            %in = train_labels(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
+            out = train_labels(:,:,:,(i-1)*config.batch_size+1:i*config.batch_size);
+            out = out((size(in, 1) - config.output_size(1)) / 2 + 1:(size(in, 1) - config.output_size(1)) / 2 + config.output_size(1), ...
+                      (size(in, 2) - config.output_size(2)) / 2 + 1:(size(in, 2) - config.output_size(2)) / 2 + config.output_size(2), :, :);
+            
+                  
+            % make the mask list
+            mask_li = {};
+            mask_li{1} = mask;
+            % operate the training pipeline
+            op_train_pipe_with_mask(in.*mask, mask_li, out);
+            % update the weights
+            config.UPDATE_WEIGHTS();
+            
+            if(cost_avg == 0)
+                cost_avg = config.cost;
+            else
+                cost_avg = (cost_avg + config.cost) / 2;
+            end
+
+            % display point
+            if(mod(points_seen, display_points) == 0)
+                count = count + 1;
+                fprintf('%d ', count);
+            end
+            % save point
+            if(mod(points_seen, save_points) == 0)
+                fprintf('\n%s', datestr(now, 'dd-mm-yyyy HH:MM:SS FFF'));
+                epoc = epoc + 1;
+                test_cost = 0;
+                for t = 1:size(test_samples, 4) / config.batch_size
+                    t_label = test_labels(:,:,:,(t-1)*config.batch_size+1:t*config.batch_size);
+                    t_label = config.NEW_MEM(t_label((size(in, 1) - config.output_size(1)) / 2 + 1:(size(in, 1) - config.output_size(1)) / 2 + config.output_size(1), ...
+                                            (size(in, 2) - config.output_size(2)) / 2 + 1:(size(in, 2) - config.output_size(2)) / 2 + config.output_size(2), :));
+                    
+                    op_test_pipe_with_mask(test_samples(:,:,:,(t-1)*config.batch_size+1:t*config.batch_size).*mask, mask_li, t_label);
+                    
+                    test_out = gather(mem.output);
+                    test_cost = test_cost + config.cost;
+                end
+                test_cost = test_cost / (size(test_samples, 4) / config.batch_size);
+                fprintf('\nepoc %d, training avg cost: %f, test avg cost: %f\n', epoc, cost_avg, test_cost);                
+                
+                save_weights(strcat('applications/Shepard_CNN/Shepard_super_res/results/shepard_layer_x4/epoc', num2str(epoc), '.mat'));
+                
+                cost_avg = 0;
+            end
+        end
+    end
+end
+