--- a
+++ b/combinedDeepLearningActiveContour/functions/stack2params.m
@@ -0,0 +1,50 @@
+function [params, netconfig] = stack2params(stack)
+
+% Converts a "stack" structure into a flattened parameter vector and also
+% stores the network configuration. This is useful when working with
+% optimization toolboxes such as minFunc.
+%
+% [params, netconfig] = stack2params(stack)
+%
+% stack - the stack structure, where stack{1}.w = weights of first layer
+%                                    stack{1}.b = weights of first layer
+%                                    stack{2}.w = weights of second layer
+%                                    stack{2}.b = weights of second layer
+%                                    ... etc.
+
+
+% Setup the compressed param vector
+params = [];
+for d = 1:numel(stack)
+    
+    % This can be optimized. But since our stacks are relatively short, it
+    % is okay
+    params = [params ; stack{d}.w(:) ; stack{d}.b(:) ];
+    
+    % Check that stack is of the correct form
+    assert(size(stack{d}.w, 1) == size(stack{d}.b, 1), ...
+        ['The bias should be a *column* vector of ' ...
+         int2str(size(stack{d}.w, 1)) 'x1']);
+    if d < numel(stack)
+        assert(size(stack{d}.w, 1) == size(stack{d+1}.w, 2), ...
+            ['The adjacent layers L' int2str(d) ' and L' int2str(d+1) ...
+             ' should have matching sizes.']);
+    end
+    
+end
+
+if nargout > 1
+    % Setup netconfig
+    if numel(stack) == 0
+        netconfig.inputsize = 0;
+        netconfig.layersizes = {};
+    else
+        netconfig.inputsize = size(stack{1}.w, 2);
+        netconfig.layersizes = {};
+        for d = 1:numel(stack)
+            netconfig.layersizes = [netconfig.layersizes ; size(stack{d}.w,1)];
+        end
+    end
+end
+
+end
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