# 神经网络-基本的Python

I am using the following tutorial for developing a basic neural network that does feedforward and backdrop. The link to the tutorial is here : Python Neural Network Tutorial

``````import numpy as np

def sigmoid(x):
return 1.0/(1+ np.exp(-x))

def sigmoid_derivative(x):
return x * (1.0 - x)

class NeuralNetwork:
def __init__(self, x, y):
self.input      = x
self.weights1   = np.random.rand(self.input.shape[1],4)
self.weights2   = np.random.rand(4,1)
self.y          = y
self.output     = np.zeros(self.y.shape)

def feedforward(self):
self.layer1 = sigmoid(np.dot(self.input, self.weights1))
self.output = sigmoid(np.dot(self.layer1, self.weights2))

def backprop(self):
# application of the chain rule to find derivative of the loss function with respect to weights2 and weights1
d_weights2 = np.dot(self.layer1.T, (2*(self.y - self.output) * sigmoid_derivative(self.output)))
d_weights1 = np.dot(self.input.T,  (np.dot(2*(self.y - self.output) * sigmoid_derivative(self.output), self.weights2.T) * sigmoid_derivative(self.layer1)))

# update the weights with the derivative (slope) of the loss function
self.weights1 += d_weights1
self.weights2 += d_weights2

if __name__ == "__main__":
X = np.array([[0,0,1],
[0,1,1],
[1,0,1],
[1,1,1]])
y = np.array([[0],[1],[1],[0]])
nn = NeuralNetwork(X,y)

for i in range(1500):
nn.feedforward()
nn.backprop()

print(nn.output)
``````

``````if __name__ == "__main__":
X = np.array([[2,4,6,8,10],
[1,3,5,7,9],
[11,13,15,17,19],
[22,24,26,28,30]])
y = np.array([[1],[0],[0],[1]])
nn = NeuralNetwork(X,y)

The output I get is :
[[0.50000001]
[0.50000002]
[0.50000001]
[0.50000001]]
``````