Convolution

Author: danluu

cnn_exercise/cnnConvolve.m

@@ -40,10 +40,8 @@
 % that you need to take into account the whitening and mean subtraction
 % steps
 
-
-
-
-
+wt = W * ZCAWhite;
+bt = b - wt * meanPatch;
 
 % --------------------------------------------------------
 
@@ -53,14 +51,15 @@
 
     % convolution of image with feature matrix for each channel
     convolvedImage = zeros(imageDim - patchDim + 1, imageDim - patchDim + 1);
-    for channel = 1:3
+    for channel = 1:imageChannels
 
       % Obtain the feature (patchDim x patchDim) needed during the convolution
       % ---- YOUR CODE HERE ----
-      feature = zeros(8,8); % You should replace this
-      
-      
-      
+      feature = zeros(patchDim, patchDim); % You should replace
+                                           % this
+      first = patchDim * patchDim * (channel - 1) + 1;
+      last = first + patchDim * patchDim - 1;
+      feature = reshape(wt(featureNum, first:last), patchDim, patchDim);
 
       % ------------------------
 
@@ -74,9 +73,8 @@
       % be sure to do a 'valid' convolution
       % ---- YOUR CODE HERE ----
 
-      
-      
-      
+      convolvedImage = convolvedImage + conv2(im, feature, 'valid');
+            
       % ------------------------
 
     end
@@ -85,9 +83,8 @@
     % Then, apply the sigmoid function to get the hidden activation
     % ---- YOUR CODE HERE ----
 
-    
-    
-    
+    convolvedImage = sigmoid(convolvedImage + bt(featureNum));
+        
     % ------------------------
 
     % The convolved feature is the sum of the convolved values for all channels
@@ -98,3 +95,6 @@
 
 end
 
+function sigm = sigmoid(x)
+    sigm = 1 ./ (1 + exp(-x));
+end