-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py
60 lines (39 loc) · 1.65 KB
/
main.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
import numpy as np
#Creating a class for our neural net
class NeuralNetwork():
def __init__(self):
np.random.seed(1)
self.synaptic_weights = 2*np.random.random((3,1)) - 1
def sigmoid(self, x):
return 1/(1 + np.exp(-x))
def sigmoid_derivative(self, x):
return x*(1-x)
def train(self, training_inputs, training_outputs, training_iterations):
for i in range(training_iterations):
output = self.think(training_inputs)
error = training_outputs - output
adjustments = np.dot(training_inputs.T , error*self.sigmoid_derivative(output))
self.synaptic_weights += adjustments
def think(self , inputs):
inputs = inputs.astype(float)
output = self.sigmoid(np.dot(inputs, self.synaptic_weights))
return output
if __name__ == "__main__":
#Initialise Neural Network
neural_network = NeuralNetwork()
print("Random Synaptic Weights:")
print(neural_network.synaptic_weights)
training_inputs = np.array([[0,0,1],
[1,1,1],
[1,0,1],
[0,1,1]])
training_outputs = np.array([[0,1,1,0]]).T
neural_network.train(training_inputs, training_outputs, 10000)
print("Synaptic weights after training:")
print(neural_network.synaptic_weights)
A = str(input("First Input:"))
B = str(input("Second Input:"))
C = str(input("Third Input:"))
print("New Scenario: Input Data=", A,B,C)
print("Output Data:")
print(neural_network.think(np.array([A , B , C])))