Added down some comments explaining the backpropogation principle

backpropogation.py

@@ -1,37 +1,48 @@
-import numpy as np
-
-def sigmoid(z):
-return 1.0/(1.0+np.exp(-z))
-
-def sigmoid_prime(z):
-return sigmoid(z)*(1-sigmoid(z))
-#The backpropogation function 
-def backprop(net, x, y):
-        '''
-        This function performs Back Propogation of a Neural Network. 
-        It takes neural net as OBJECT and training data as List as argument 
-        and returns neural net as OBJECT and derivative of cost wrt to Bias (nabla_b) and weights(nabla_w) as numpy array.
-        Use:
-        `net,nabla_b, nabla_w = backprop(net, x, y)`
-        '''
-        nabla_b = [np.zeros(b.shape) for b in net.biases]
-        nabla_w = [np.zeros(w.shape) for w in net.weights]
-        activation = x
-        activations = [x] 
-        zs = [] 
-        for b, w in zip(net.biases, net.weights):
-            z = np.dot(w, activation)+b
-            zs.append(z)
-            activation = sigmoid(z)
-            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())
-        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)
+"""backpropogation.py
+
+Using it the Neural Network is trained by altering Weights and Biases to minimize the cost function.
+This is done by going in backward direction from output to input layer 
+and altering the Weights and Biases to get the desired output of the training data.
+"""
+
+import numpy as np
+
+def sigmoid(z):
+    """
+    This is an Input ,Output funtion which gives output between 0 and 1 
+    It basically represents the activation of that neuron.
+    """
+    return 1.0/(1.0+np.exp(-z))
+
+def sigmoid_prime(z):
+    return sigmoid(z)*(1-sigmoid(z))
+#The backpropogation function 
+def backprop(net, x, y):
+        """
+        This function performs Back Propogation of a Neural Network. 
+        It takes neural net as OBJECT and training data as List as argument 
+        and returns neural net as OBJECT and derivative of cost wrt to Bias (nabla_b) and weights(nabla_w) as numpy array.
+        Use:
+        `net,nabla_b, nabla_w = backprop(net, x, y)`        
+        """
+        nabla_b = [np.zeros(b.shape) for b in net.biases]
+        nabla_w = [np.zeros(w.shape) for w in net.weights]
+        activation = x
+        activations = [x] 
+        zs = [] 
+        for b, w in zip(net.biases, net.weights):
+            z = np.dot(w, activation)+b
+            zs.append(z)
+            activation = sigmoid(z)
+            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())
+        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)