python搭建简易神经网络结构

本节使用python环境，在不使用深度学习工具箱情况下搭建一个简单的神经网络结构（非CNN卷积网络）来训练mnist手写体数据库。

网络的结构可以很简单，比如就是([784,200,100,10])，输入维度为784是一个样本大小的28*28，网络包含dropout操作，更多的是理解这种最基础的反向传播机制的实现过程。

完整的项目点击github主页获取

下面看下可运行的包含训练测试的代码：

# -*- coding: utf-8 -*-"""@author: chen"""import numpy as npimport structfrom datetime import datetimeimport matplotlib.pyplot as plt#读取图像def read_image(filename):    binfile = open(filename , 'rb')    buf = binfile.read()    index = 0    magic, numImages , numRows , numColumns = struct.unpack_from('>IIII' , buf , index)    index += struct.calcsize('>IIII')    data = np.zeros((numImages,numRows*numColumns))    for i in range(numImages):         im = struct.unpack_from('>784B' ,buf, index)        index += struct.calcsize('>784B')        im = np.array(im)        data[i,:] = im    return data#读取图像labeldef read_label(filename):    binfile = open(filename , 'rb')    buf = binfile.read()    index = 0    magic, numLabels = struct.unpack_from('>II' , buf , index)    index += struct.calcsize('>II')    data = np.zeros((numLabels,10))    for i in range(numLabels):         label = struct.unpack_from('>B' ,buf, index)[0]        label = np.array(label)        data[i,label] = 1        index += struct.calcsize('>B')    return data# 建立与初始化网络参数    class nn_setup():    def __init__(self,net,learningRate = 2, epochs = 100, batch = 100, dropoutFraction = 0.05):        self.net = net        self.size = net.size        self.learningRate = learningRate        self.dropoutFraction = dropoutFraction        self.epochs = epochs        self.batch = batch        # 权值以list的形式保存，方便不同层之间的矩阵参数索引        self.W = list()         self.a = list()        self.d = list()        self.dW = list()        self.dropoutMask = list()        self.L = 0        # 初始化网络参数        for i in range(1,self.size):            weight = (np.random.rand(self.net[i], self.net[i - 1]+1) - 0.5) * 2 * 4 * np.sqrt(6 / (self.net[i] + self.net[i - 1]))            self.W.append(weight)            weight = np.zeros([self.net[i], self.net[i - 1]+1])            self.dW.append(weight)        for i in range(self.size):            if i == self.size-1:                a_weight = np.zeros([self.batch, self.net[i]])            else:                      a_weight = np.zeros([self.batch, self.net[i]+1])            self.a.append(a_weight)        if self.dropoutFraction > 0:            for i in range(self.size):                if i == self.size-1:                    dropout_weight = np.zeros([self.batch, self.net[i]])                else:                          dropout_weight = np.zeros([self.batch, self.net[i]+1])                self.dropoutMask.append(dropout_weight)        for i in range(self.size):            if i == self.size-1:                d_weight = np.zeros([self.batch, self.net[i]])            else:                      d_weight = np.zeros([self.batch, self.net[i]+1])            self.d.append(d_weight)         self.e = np.zeros(self.batch,self.net[self.size - 1])def sigmoid(inputs):    row,col = inputs.shape    for i in range(row):        for j in range(col):            inputs[i,j] = 1 / (1 + np.exp(- inputs[i,j]))    return inputs##----------------------------------------------------------------if __name__ == '__main__':    # 数据库文件夹选择    filename_traindata = 'MNIST_data/train-images.idx3-ubyte'    filename_trainlabel = 'MNIST_data/train-labels.idx1-ubyte'    filename_testdata = 'MNIST_data/t10k-images.idx3-ubyte'    filename_testlabel = 'MNIST_data/t10k-labels.idx1-ubyte'    train_data = read_image(filename_traindata)/255;    train_label = read_label(filename_trainlabel)    test_data = read_image(filename_testdata)/255;    test_label = read_label(filename_testlabel)    # 自定义网络结构与网络参数    net = np.array([784,200,100,10])       learningRate = 2 #学习率    batch = 100 #batch大小    epochs = 100 #迭代次数    dropoutFraction = 0.05 #dropout率    # 初始化网络    nn = nn_setup(net,learningRate = learningRate,batch = batch,epochs = epochs)    plot_flag = 0 #是否图像画出中间结果 0-不画     Loss = np.array([])    accuracy_all = np.array([])    ##----------------------训练----------------------------    for epochs in range(nn.epochs):        time_start = datetime.now()  #记录训练开始时间        num = int(np.floor(train_data.shape[0]/nn.batch))        for num_batch in range(num) :            choose = np.random.randint(1,train_data.shape[0],nn.batch)             batch_x = train_data[choose,:]            batch_y = train_label[choose,:]          ##--------------------nn前向传播计算各层输出值---------------            m = batch_x.shape[0]            nn.a[0] = np.hstack((np.ones([m,1]),batch_x))            #从前往后依次计算各层输出            for i in range(1,nn.size-1):                nn.a[i] = sigmoid(np.dot(nn.a[i-1],nn.W[i-1].T))                if nn.dropoutFraction > 0:                    nn.dropoutMask[i] = np.random.rand(nn.a[i].shape[0],nn.a[i].shape[1])                    nn.dropoutMask[i][nn.dropoutMask[i] > nn.dropoutFraction] = 1                    nn.dropoutMask[i][nn.dropoutMask[i] <= nn.dropoutFraction] = 0                    nn.a[i] = nn.a[i] * nn.dropoutMask[i]                nn.a[i] = np.hstack((np.ones([m,1]),nn.a[i]))            # 计算最后一层的误差                nn.a[nn.size-1] = sigmoid(np.dot(nn.a[nn.size-2],nn.W[nn.size-2].T))            nn.e = batch_y - nn.a[nn.size-1] #误差计算            nn.L = 1/2 * np.sum(nn.e * nn.e)/m            Loss = np.hstack((Loss,nn.L))    ##---------------------nn反向传播计算各层梯度----------------            nn.d[nn.size-1] = - nn.e * (nn.a[nn.size-1] * (1 - nn.a[nn.size-1]))            # 从后往前依次计算反向传播的各层梯度            for i in range(nn.size-2,0,-1):                d_act = nn.a[i] * (1 - nn.a[i])                if i+1 == nn.size-1:                    nn.d[i] = np.dot(nn.d[i+1],nn.W[i]) * d_act                else:                    nn.d[i] = np.dot(nn.d[i+1][:,1:],nn.W[i]) * d_act                if nn.dropoutFraction > 0:                    nn.d[i] = nn.d[i] * np.hstack((np.ones([nn.d[i].shape[0],1]),nn.dropoutMask[i]))            for i in range(nn.size-2):                if i+1 == nn.size-1:                    nn.dW[i] = np.dot(nn.d[i + 1].T , nn.a[i]) / nn.d[i + 1].shape[0]                else:                    nn.dW[i] = np.dot(nn.d[i + 1][:,1:].T , nn.a[i]) / nn.d[i + 1].shape[0]    ##-------------------nn计算各层梯度更新-------------------            for i in range(nn.size-2):                dW = nn.dW[i]                dW = nn.learningRate * dW                nn.W[i] = nn.W[i] - dW                       # 相关结果输出            if num_batch % 100 == 0:                print('epochs = ', epochs,' / ', nn.epochs,                        '; batch = ',num_batch,' / ',num,                        '; error_batch = ', nn.L)        time_end = datetime.now()        print('time using for this epoch = ', (time_end.minute -time_start.minute)*60 +               (time_end.second-time_start.second) +             (time_end.microsecond - time_start.microsecond)/1000000, 's')    ##-------------------计算测试样本的准确率-----------------        m = test_data.shape[0]        nn.a[0] = np.hstack((np.ones([m,1]),test_data))        for i in range(1,nn.size-1):            nn.a[i] = sigmoid(np.dot(nn.a[i-1],nn.W[i-1].T))            nn.a[i] = nn.a[i] * (1-nn.dropoutFraction)            nn.a[i] = np.hstack((np.ones([m,1]),nn.a[i]))        nn.a[nn.size-1] = sigmoid(np.dot(nn.a[nn.size-2],nn.W[nn.size-2].T))        res = nn.a[nn.size-1]        pre_y = np.zeros(res.shape[0])        y_label = np.zeros(res.shape[0])        count = 0        for i in range(res.shape[0]):            pre_y[i] = np.argmax(res[i,:])            y_label[i] = np.argmax(test_label[i,:])            if pre_y[i] == y_label[i]:                count = count + 1        accuracy = count/y_label.size        accuracy_all = np.hstack((accuracy_all,accuracy))        print('-----------------------------------------\n',        'test accuracy = ', accuracy, '(',count,'/',y_label.size,')',        '\n-----------------------------------------\n')        if plot_flag:            plt.figure(1)            plt.plot(Loss)            plt.title("training batch error")            plt.figure(2)            plt.plot(accuracy_all)            plt.title("testing accuracy in different epochs")            plt.show()