[python] a little deep learning case

from numpy import exp, array, random, dot

class NeuralNetwork():
    def __init__(self):
        random.seed(1)
        self.synaptic_weights = 2 * random.random((3,1)) - 1
    
    def __sigmoid(self, x):
        return 1 / (1 + exp(-x))
    
    def __sigmoid_derivative(self, x):
        return x*(1-x)
    
    def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):
        for iteration in range(number_of_training_iterations):
            output = self.think(training_set_inputs)
            error = training_set_outputs - output
            adjustment = dot(training_set_inputs.T, error*self.__sigmoid_derivative(output))
            self.synaptic_weights += adjustment
    
    def think(self, inputs):
        return self.__sigmoid(dot(inputs, self.synaptic_weights))
    

if __name__ == '__main__':
    neural_network = NeuralNetwork()
    print('随机的初始突触权重')
    print(neural_network.synaptic_weights)
    
    training_set_inputs = array([[0,0,1], [1,1,1], [1,0,1], [0,1,1]])
    training_set_outputs = array([[0,1,1,0]]).T
    
    neural_network.train(training_set_inputs, training_set_outputs, 10000)
    
    print('训练后的突触权重')
    print(neural_network.synaptic_weights)
    
    print('考虑新的形势[1, 0, 0]')
    print(neural_network.think(array([1, 0, 0])))