CV学习笔记-BP神经网络代码Python实例
一. 任务描述
给定数据集（txt文件），采用随机梯度下降的方式进行神经网络的学习，完成权重参数的更新，使得输入的数据能够接近输出label。
关于BP神经网络的手推和原理见笔者《CV学习笔记-推理和训练》、《CV学习笔记-BP神经网络》
txt文件类似下图所示
| col1 | col2 | col3 |
| 输入1 | 输入2 | label |
二. 程序设计
1. 神经网络设计
类：NeuralNetWork
类内初始化： __init__用以设置神经网络的参数（输入层参数、隐藏层参数、输出层参数、学习率）
类内方法： train用于训练数据，更新权重
读取数据： loadDataSet用于在txt文件中读取数据，包括输入值和label值
随机梯度下降处理： stocGradDescent用于处理训练数据的过程
2. 具体设计
- NeuralNetWork类
class NeuralNetWork: def __init__(self, input_nodes, hidden_nodes, out_nodes, lr): # 设置输入个数 self.innodes = input_nodes # 设置隐藏层节点个数 self.hnodes = hidden_nodes # 设置输出节点个数 self.onodes = out_nodes # 设置学习率，用于反向更新 self.lr = lr # self.weight_i2h = np.ones((self.hnodes, self.innodes)) # self.weight_h2o = np.ones((self.onodes, self.hnodes)) # 随机初始化比1矩阵效果要好很多 # 权重矩阵（输入到隐藏） self.weight_i2h = (numpy.random.normal(0.0, pow(self.hnodes,-0.5), (self.hnodes,self.innodes) ) ) # 权重矩阵（隐藏到输出） self.weight_h2o = (numpy.random.normal(0.0, pow(self.onodes,-0.5), (self.onodes,self.hnodes) ) ) # 设置激活函数（sigmoid） self.activation_function = lambda x: 1.0/(1+np.exp(-x)) pass ''' 训练方法，输入一次训练的输入和label ''' def train(self, inputs_list, targets_list): inputs = numpy.array(inputs_list, ndmin=2).T target = np.array(targets_list, ndmin=2).T # wx+b hidden_inputs = np.dot(self.weight_i2h, inputs) # 激活作为隐藏层的输出 hidden_outputs = self.activation_function(hidden_inputs) # wx+b o_inputs = np.dot(self.weight_h2o, hidden_outputs) # 激活作为输出 o_outputs = self.activation_function(o_inputs) # 损失函数 loss = (target - o_outputs) ** 2 * 0.5 # 输出误差，用于反向更新 error = target - o_outputs # error = target - o_outputs # 隐藏层误差，用于反向更新 hidden_error = np.dot(self.weight_h2o.T, error * o_outputs * (1 - o_outputs)) # 梯度 gradO = error * o_outputs * (1 - o_outputs) # 反向更新，详见笔者博客[《CV学习笔记-BP神经网络》(https://blog.csdn.net/qq_38853759/article/details/121930413) self.weight_h2o += self.lr * np.dot((error * o_outputs * (1 - o_outputs)), np.transpose(hidden_outputs)) gradI = hidden_error * hidden_outputs * (1 - hidden_outputs) # 反向更新 self.weight_i2h += self.lr * np.dot((hidden_error * hidden_outputs * (1 - hidden_outputs)), np.transpose(inputs)) return loss
- 读取数据集并处理
def loadDataSet(): data = [] label = [] fr = open('testSet.txt') for line in fr.readlines(): lineArr = line.strip().split() data.append([1.0, float(lineArr[0]), float(lineArr[1])]) label.append(int(lineArr[2])) data = np.array(data) label = np.array(label) return data, label
- 训练过程处理
def stocGradDescent(data, label): m, n = np.shape(data) for iter in range(200): total_loss = 0 for i in range(m): # if label[i] == 1: # pass # elif label[i] == 0: # pass # 累计每个epoch的loss观察效果 total_loss += net.train(data[i], label[i]) print("NO.{} Loss={}".format(iter, total_loss))
三、实践代码
import numpy import numpy as np class NeuralNetWork: def __init__(self, input_nodes, hidden_nodes, out_nodes, lr): self.innodes = input_nodes self.hnodes = hidden_nodes self.onodes = out_nodes self.lr = lr # self.weight_i2h = np.ones((self.hnodes, self.innodes)) # self.weight_h2o = np.ones((self.onodes, self.hnodes)) # 随机初始化比1矩阵效果要好很多 self.weight_i2h = (numpy.random.normal(0.0, pow(self.hnodes,-0.5), (self.hnodes,self.innodes) ) ) self.weight_h2o = (numpy.random.normal(0.0, pow(self.onodes,-0.5), (self.onodes,self.hnodes) ) ) self.activation_function = lambda x: 1.0/(1+np.exp(-x)) pass def train(self, inputs_list, targets_list): inputs = numpy.array(inputs_list, ndmin=2).T target = np.array(targets_list, ndmin=2).T hidden_inputs = np.dot(self.weight_i2h, inputs) hidden_outputs = self.activation_function(hidden_inputs) o_inputs = np.dot(self.weight_h2o, hidden_outputs) o_outputs = self.activation_function(o_inputs) loss = (target - o_outputs) ** 2 * 0.5 error = target - o_outputs # error = target - o_outputs hidden_error = np.dot(self.weight_h2o.T, error * o_outputs * (1 - o_outputs)) gradO = error * o_outputs * (1 - o_outputs) self.weight_h2o += self.lr * np.dot((error * o_outputs * (1 - o_outputs)), np.transpose(hidden_outputs)) gradI = hidden_error * hidden_outputs * (1 - hidden_outputs) self.weight_i2h += self.lr * np.dot((hidden_error * hidden_outputs * (1 - hidden_outputs)), np.transpose(inputs)) return loss # 从testSet.txt中读取数据存储至样本集data和标签集label def loadDataSet(): data = [] label = [] fr = open('testSet.txt') for line in fr.readlines(): lineArr = line.strip().split() data.append([1.0, float(lineArr[0]), float(lineArr[1])]) label.append(int(lineArr[2])) data = np.array(data) label = np.array(label) return data, label def stocGradDescent(data, label): m, n = np.shape(data) for iter in range(200): total_loss = 0 for i in range(m): # if label[i] == 1: # pass # elif label[i] == 0: # pass total_loss += net.train(data[i], label[i]) print("NO.{} Loss={}".format(iter, total_loss)) if __name__ == '__main__': input_nodes = 3 hidden_nodes = 3 output_nodes = 1 learning_rate = 0.1 net = NeuralNetWork(input_nodes, hidden_nodes, output_nodes, learning_rate) data, label = loadDataSet() stocGradDescent(data, label) print(net.weight_i2h) print(net.weight_h2o) pass
txt文本文件内容请自行复制
-0.017612 14.053064 0 -1.395634 4.662541 1 -0.752157 6.538620 0 -1.322371 7.152853 0 0.423363 11.054677 0 0.406704 7.067335 1 0.667394 12.741452 0 -2.460150 6.866805 1 0.569411 9.548755 0 -0.026632 10.427743 0 0.850433 6.920334 1 1.347183 13.175500 0 1.176813 3.167020 1 -1.781871 9.097953 0 -0.566606 5.749003 1 0.931635 1.589505 1 -0.024205 6.151823 1 -0.036453 2.690988 1 -0.196949 0.444165 1 1.014459 5.754399 1 1.985298 3.230619 1 -1.693453 -0.557540 1 -0.576525 11.778922 0 -0.346811 -1.678730 1 -2.124484 2.672471 1 1.217916 9.597015 0 -0.733928 9.098687 0 -3.642001 -1.618087 1 0.315985 3.523953 1 1.416614 9.619232 0 -0.386323 3.989286 1 0.556921 8.294984 1 1.224863 11.587360 0 -1.347803 -2.406051 1 1.196604 4.951851 1 0.275221 9.543647 0 0.470575 9.332488 0 -1.889567 9.542662 0 -1.527893 12.150579 0 -1.185247 11.309318 0 -0.445678 3.297303 1 1.042222 6.105155 1 -0.618787 10.320986 0 1.152083 0.548467 1 0.828534 2.676045 1 -1.237728 10.549033 0 -0.683565 -2.166125 1 0.229456 5.921938 1 -0.959885 11.555336 0 0.492911 10.993324 0 0.184992 8.721488 0 -0.355715 10.325976 0 -0.397822 8.058397 0 0.824839 13.730343 0 1.507278 5.027866 1 0.099671 6.835839 1 -0.344008 10.717485 0 1.785928 7.718645 1 -0.918801 11.560217 0 -0.364009 4.747300 1 -0.841722 4.119083 1 0.490426 1.960539 1 -0.007194 9.075792 0 0.356107 12.447863 0 0.342578 12.281162 0 -0.810823 -1.466018 1 2.530777 6.476801 1 1.296683 11.607559 0 0.475487 12.040035 0 -0.783277 11.009725 0 0.074798 11.023650 0 -1.337472 0.468339 1 -0.102781 13.763651 0 -0.147324 2.874846 1 0.518389 9.887035 0 1.015399 7.571882 0 -1.658086 -0.027255 1 1.319944 2.171228 1 2.056216 5.019981 1 -0.851633 4.375691 1 -1.510047 6.061992 0 -1.076637 -3.181888 1 1.821096 10.283990 0 3.010150 8.401766 1 -1.099458 1.688274 1 -0.834872 -1.733869 1 -0.846637 3.849075 1 1.400102 12.628781 0 1.752842 5.468166 1 0.078557 0.059736 1 0.089392 -0.715300 1 1.825662 12.693808 0 0.197445 9.744638 0 0.126117 0.922311 1 -0.679797 1.220530 1 0.677983 2.556666 1 0.761349 10.693862 0 -2.168791 0.143632 1 1.388610 9.341997 0 0.317029 14.739025 0
四、 效果测试
通过debug看效果：
可见刚开始网络的输出跟label的差距还是很大
可以看到训练到一百多代的时候loss已经有了明显的下降
此时debug看效果，可以看到效果明显改善，网络的输出已经比较小接近于真实label：0了
label为1的效果也靠谱很多了
最后打印出权重参数：
