1+ import numpy
2+
3+ class NeuralNetwork :
4+
5+ def __init__ (self , input_array , output_array ):
6+ '''
7+ input_array : input values for training the neural network.
8+ output_array : expected output values of the given inputs.
9+ '''
10+ self .input = input_array
11+ #Initial weights are assigned randomly where first argument is the number of nodes in previous layer
12+ #and second argument is the number of nodes in the next layer.
13+
14+ #random initial weights for the input layer
15+ #self.input.shape[1] is used to reprsent number of nodes in input layer
16+ #first hidden layer consists of 4 nodes
17+ self .weights1 = numpy .random .rand (self .input .shape [1 ],4 )
18+
19+ #random initial weights for the first hidden layer
20+ #first hidden layer has 4 nodes
21+ #second hidden layer has 3 nodes
22+ self .weights2 = numpy .random .rand (4 ,3 )
23+
24+ #random inital weights for the second hidden layer
25+ #second hidden layer has 3 nodes
26+ #output layer has 1 node
27+ self .weights3 = numpy .random .rand (3 ,1 )
28+
29+ self .y = output_array
30+ self .output = numpy .zeros (output_array .shape )
31+
32+ def feedforward (self ):
33+ '''
34+ feedforward propagation using sigmoid activation function between layers
35+ return the last layer of the neural network
36+ '''
37+ #layer1 is the layer connecting the input nodes with the first hidden layer nodes
38+ self .layer1 = sigmoid (numpy .dot (self .input , self .weights1 ))
39+
40+ #layer2 is the layer connecting the first hidden set of nodes with the second hidden set of nodes
41+ self .layer2 = sigmoid (numpy .dot (self .layer1 , self .weights2 ))
42+
43+ #layer3 is the layer connecting second hidden layer with the output node
44+ self .layer3 = sigmoid (numpy .dot (self .layer2 ,self .weights3 ))
45+
46+ return self .layer3
47+
48+ def back_propagation (self ):
49+ '''
50+ backpropagating between the layers using sigmoid derivative and loss between layers
51+ updates the weights between the layers
52+ '''
53+
54+ updated_weights3 = numpy .dot (self .layer2 .T ,2 * (self .y - self .output )* sigmoid_derivative (self .output ))
55+ updated_weights2 = numpy .dot (self .layer1 .T , numpy .dot (2 * (self .y - self .output )* sigmoid_derivative (self .output ), self .weights3 .T )* sigmoid_derivative (self .layer2 ))
56+ updated_weights1 = numpy .dot (self .input .T , numpy .dot (numpy .dot (2 * (self .y - self .output )* sigmoid_derivative (self .output ), self .weights3 .T )* sigmoid_derivative (self .layer2 ),self .weights2 .T )* sigmoid_derivative (self .layer1 ))
57+
58+ self .weights1 += updated_weights1
59+ self .weights2 += updated_weights2
60+ self .weights3 += updated_weights3
61+
62+ def train (self , output , iterations ):
63+ '''
64+ output : required for calculating loss
65+ performs the feeding and back propagation process for the given number of iterations
66+ every iteration will update the weights of neural network
67+ '''
68+ for iteration in range (1 ,iterations + 1 ):
69+ self .output = self .feedforward ()
70+ self .back_propagation ()
71+ print ("Iteration %s " % iteration ,"Loss: " + str (numpy .mean (numpy .square (output - self .feedforward ()))))
72+
73+ def predict (self , input ):
74+ '''
75+ predict output for the given input values
76+ '''
77+ self .array = input
78+ self .layer1 = sigmoid (numpy .dot (self .array , self .weights1 ))
79+ self .layer2 = sigmoid (numpy .dot (self .layer1 , self .weights2 ))
80+ self .layer3 = sigmoid (numpy .dot (self .layer2 ,self .weights3 ))
81+ return self .layer3
82+
83+ def sigmoid (value ):
84+ '''
85+ applies sigmoid activation function
86+ return normalized values
87+ '''
88+ return 1 / (1 + numpy .exp (- value ))
89+
90+ def sigmoid_derivative (value ):
91+ '''
92+ returns derivative of the sigmoid value
93+ '''
94+ return sigmoid (value )* (1 - sigmoid (value ))
95+
96+
97+ if __name__ == "__main__" :
98+
99+ #input values
100+ input = numpy .array (([0 ,0 ,0 ],[0 ,0 ,1 ],[0 ,1 ,0 ],[0 ,1 ,1 ],
101+ [1 ,0 ,0 ],[1 ,0 ,1 ],[1 ,1 ,0 ],[1 ,1 ,1 ]),dtype = float )
102+
103+ #true output for the given input values
104+ output = numpy .array (([0 ],[1 ],[1 ],[0 ],
105+ [1 ],[0 ],[0 ],[1 ]),dtype = float )
106+
107+ #calling neural network class
108+ Neural_Network = NeuralNetwork (input_array = input , output_array = output )
109+
110+ #calling training function
111+ Neural_Network .train (output = output , iterations = 1000 )
112+ print (Neural_Network .predict ([0 ,1 ,1 ]))
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