kenorb · December 4, 2020 18:04 · Feb 20, 2018 · Dec 14, 2017
diff --git a/backward_pass.py b/backward_pass.py
@@ -30,7 +30,7 @@ def conv_backward(dH, cache):
     # Looping over vertical(h) and horizontal(w) axis of the output
     for h in range(n_H):
         for w in range(n_W):
-            dX[h:h+f, w:w+f] += W * dZ(h,w)
-            dW += X[h:h+f, w:w+f] * dZ(h,w)
+            dX[h:h+f, w:w+f] += W * dH(h,w)
+            dW += X[h:h+f, w:w+f] * dH(h,w)
 
     return dX, dW
diff --git a/backward_pass.py b/backward_pass.py
@@ -0,0 +1,36 @@
+def conv_backward(dH, cache):
+    '''
+    The backward computation for a convolution function
+    
+    Arguments:
+    dH -- gradient of the cost with respect to output of the conv layer (H), numpy array of shape (n_H, n_W) assuming channels = 1
+    cache -- cache of values needed for the conv_backward(), output of conv_forward()
+    
+    Returns:
+    dX -- gradient of the cost with respect to input of the conv layer (X), numpy array of shape (n_H_prev, n_W_prev) assuming channels = 1
+    dW -- gradient of the cost with respect to the weights of the conv layer (W), numpy array of shape (f,f) assuming single filter
+    '''
+
+    # Retrieving information from the "cache"
+    (X, W) = cache
+
+    # Retrieving dimensions from X's shape
+    (n_H_prev, n_W_prev) = X.shape
+
+    # Retrieving dimensions from W's shape
+    (f, f) = W.shape
+
+    # Retrieving dimensions from dH's shape
+    (n_H, n_W) = dH.shape
+
+    # Initializing dX, dW with the correct shapes
+    dX = np.zeros(X.shape)
+    dW = np.zeros(W.shape)
+
+    # Looping over vertical(h) and horizontal(w) axis of the output
+    for h in range(n_H):
+        for w in range(n_W):
+            dX[h:h+f, w:w+f] += W * dZ(h,w)
+            dW += X[h:h+f, w:w+f] * dZ(h,w)
+
+    return dX, dW