python实现人工神经网络的全过程：

from numpy import *

class NeuralNet(object):

def __init__(self):

# Generate random numbers

random.seed(1)

# Assign random weights to a 3 x 1 matrix,

self.synaptic_weights = 2 * random.random((3, 1)) - 1

# The Sigmoid function

def __sigmoid(self, x):

return 1 / (1 + exp(-x))

# The derivative of the Sigmoid function.

# This is the gradient of the Sigmoid curve.

def __sigmoid_derivative(self, x):

return x * (1 - x)

# Train the neural network and adjust the weights each time.

def train(self, inputs, outputs, training_iterations):

for iteration in xrange(training_iterations):

# Pass the training set through the network.

output = self.learn(inputs)

# Calculate the error

error = outputs - output

# Adjust the weights by a factor

factor = dot(inputs.T, error * self.__sigmoid_derivative(output))

self.synaptic_weights += factor

# The neural network thinks.

def learn(self, inputs):

return self.__sigmoid(dot(inputs, self.synaptic_weights))

if __name__ == "__main__":

#Initialize

neural_network = NeuralNet()

# The training set.

inputs = array([[0, 1, 1], [1, 0, 0], [1, 0, 1]])

outputs = array([[1, 0, 1]]).T

# Train the neural network

neural_network.train(inputs, outputs, 10000)

# Test the neural network with a test example.

print neural_network.learn(array([1, 0, 1]))