Neural Net

import numpy as np

# define the sigmoid function
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# define the derivative of the sigmoid function
def sigmoid_derivative(x):
    return x * (1 - x)

# define the neural net
class NeuralNet:
    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):
        # calculate the error
        error = self.y - self.output
        
        # calculate the gradient
        gradient = sigmoid_derivative(self.output)
        gradient *= error
        gradient = gradient.mean()
        
        # adjust the weights
        self.weights2 += gradient
        self.weights1 += np.dot(self.input.T, np.dot(error, self.weights2.T) * sigmoid_derivative(self.layer1))
        
# define the input and expected output
x = np.array([[0,0,1],
              [0,1,1],
              [1,0,1],
              [1,1,1]])
y = np.array([[0],[1],[1],[0]])

# create the neural net
nn = NeuralNet(x, y)

# train the neural net
for i in range(1000):
    nn.feedforward()
    nn.backprop()

# print the output of the trained neural net
print(nn.output)