基于python的BP神经网络及异或实现过程解析

BP神经网络是最简单的神经网络模型了，三层能够模拟非线性函数效果。难点：如何确定初

BP神经网络是最简单的神经网络模型了，三层能够模拟非线性函数效果。

难点：

• 如何确定初始化参数？
• 如何确定隐含层节点数量？
• 迭代多少次？如何更快收敛？
• 如何获得全局最优解？

'''
neural networks 

created on 2019.9.24
author: vince
'''
import math
import logging
import numpy 
import random
import matplotlib.pyplot as plt

'''
neural network 
'''
class NeuralNetwork:

 def __init__(self, layer_nums, iter_num = 10000, batch_size = 1):
 self.__ILI = 0;
 self.__HLI = 1;
 self.__OLI = 2;
 self.__TLN = 3;

 if len(layer_nums) != self.__TLN:
 raise Exception("layer_nums length must be 3");

 self.__layer_nums = layer_nums; #array [layer0_num, layer1_num ...layerN_num]
 self.__iter_num = iter_num;
 self.__batch_size = batch_size;
 
 def train(self, X, Y):
 X = numpy.array(X);
 Y = numpy.array(Y);

 self.L = [];
 #initialize parameters
 self.__weight = [];
 self.__bias = [];
 self.__step_len = [];
 for layer_index in range(1, self.__TLN):
 self.__weight.append(numpy.random.rand(self.__layer_nums[layer_index - 1], self.__layer_nums[layer_index]) * 2 - 1.0);
 self.__bias.append(numpy.random.rand(self.__layer_nums[layer_index]) * 2 - 1.0);
 self.__step_len.append(0.3);

 logging.info("bias:%s" % (self.__bias));
 logging.info("weight:%s" % (self.__weight));

 for iter_index in range(self.__iter_num):
 sample_index = random.randint(0, len(X) - 1);
 logging.debug("-----round:%s, select sample %s-----" % (iter_index, sample_index));
 output = self.forward_pass(X[sample_index]);
 g = (-output[2] + Y[sample_index]) * self.activation_drive(output[2]);
 logging.debug("g:%s" % (g));
 for j in range(len(output[1])):
 self.__weight[1][j] += self.__step_len[1] * g * output[1][j];
 self.__bias[1] -= self.__step_len[1] * g;

 e = [];
 for i in range(self.__layer_nums[self.__HLI]):
 e.append(numpy.dot(g, self.__weight[1][i]) * self.activation_drive(output[1][i]));
 e = numpy.array(e);
 logging.debug("e:%s" % (e));
 for j in range(len(output[0])):
 self.__weight[0][j] += self.__step_len[0] * e * output[0][j];
 self.__bias[0] -= self.__step_len[0] * e;

 l = 0;
 for i in range(len(X)):
 predictions = self.forward_pass(X[i])[2];
 l += 0.5 * numpy.sum((predictions - Y[i]) ** 2);
 l /= len(X);
 self.L.append(l);

 logging.debug("bias:%s" % (self.__bias));
 logging.debug("weight:%s" % (self.__weight));
 logging.debug("loss:%s" % (l));
 logging.info("bias:%s" % (self.__bias));
 logging.info("weight:%s" % (self.__weight));
 logging.info("L:%s" % (self.L));
 
 def activation(self, z):
 return (1.0 / (1.0 + numpy.exp(-z)));

 def activation_drive(self, y):
 return y * (1.0 - y);

 def forward_pass(self, x):
 data = numpy.copy(x);
 result = [];
 result.append(data);
 for layer_index in range(self.__TLN - 1):
 data = self.activation(numpy.dot(data, self.__weight[layer_index]) - self.__bias[layer_index]);
 result.append(data);
 return numpy.array(result);

 def predict(self, x):
 return self.forward_pass(x)[self.__OLI];


def main():
 logging.basicConfig(level = logging.INFO,
 format = '%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
 datefmt = '%a, %d %b %Y %H:%M:%S');
 
 logging.info("trainning begin.");
 nn = NeuralNetwork([2, 2, 1]);
 X = numpy.array([[0, 0], [1, 0], [1, 1], [0, 1]]);
 Y = numpy.array([0, 1, 0, 1]);
 nn.train(X, Y);

 logging.info("trainning end. predict begin.");
 for x in X:
 print(x, nn.predict(x));

 plt.plot(nn.L)
 plt.show();

if __name__ == "__main__":
 main();

具体收敛效果

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