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neural_network.py
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import numpy as np
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 iteration 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__":
neural_network = NeuralNetwork()
print('Random synpatic 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, 100000)
print('Synaptic weights after training: ')
print(neural_network.synaptic_weights)
A = str(input('Input 1: '))
B = str(input('Input 2: '))
C = str(input('Input 3: '))
print('New situation: input data: = ', A, B, C)
print('Output data: ')
print(neural_network.think(np.array([A, B, C])))