Solving the documentation issue

backpropogation.py

@@ -5,6 +5,7 @@ def sigmoid(z):
 
 def sigmoid_prime(z):
 return sigmoid(z)*(1-sigmoid(z))
+
 #The backpropogation function 
 def backprop(net, x, y):
         '''
@@ -20,18 +21,24 @@ def backprop(net, x, y):
         activations = [x] 
         zs = [] 
         for b, w in zip(net.biases, net.weights):
-            z = np.dot(w, activation)+b
+                z = np.dot(w, activation)+b`   
             zs.append(z)
-            activation = sigmoid(z)
+            activation = sigmoid(z)          #calculating activations for the given layer
             activations.append(activation)
-        delta = net.cost_derivative(activations[-1], y) * \
-            sigmoid_prime(zs[-1])
-        nabla_b[-1] = delta
-        nabla_w[-1] = np.dot(delta, activations[-2].transpose())
+        delta = net.cost_derivative(activations[-1], y) *(sigmoid_prime(zs[-1]))      #calculating delta for the output layer
+        nabla_b[-1] = delta                                                           #calculating delta[b] for output layer
+        nabla_w[-1] = np.dot(delta, activations[-2].transpose())                      #calculating delta[w] for output layers
+
+
+        """
+        Similarly calculating
+        delta[b],delta[w] for other hidden layers
+
+        """
         for l in range(2, net.num_layers):
             z = zs[-l]
             sp = sigmoid_prime(z)
             delta = np.dot(net.weights[-l+1].transpose(), delta) * sp
             nabla_b[-l] = delta
             nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())
-        return (net,nabla_b, nabla_w)
+        return (net,nabla_b, nabla_w)                                             #returning network, delta[b],delta[w] for the whole network

network.py

@@ -67,9 +67,9 @@ def save(self, filename):
          data = {"sizes": self.sizes,
                 "weights": [w.tolist() for w in self.weights],
                 "biases": [b.tolist() for b in self.biases]}
-        f = open(filename, "w")
-        json.dump(data, f)
-        f.close()
+         f = open(filename, "w")
+         json.dump(data, f)
+         f.close()
 
 def load(filename):
     f = open(filename, "r")