Python3 卷积神经网络卷积层，池化层，全连接层前馈实现

 # -*- coding: utf-8 -*-
 """
 Created on Sun Mar  4 09:21:41 2018

 @author: markli
 """
 import numpy as np;

 def ReLU(x):
     return max(0,x);

 def logistic(x):
     return 1/(1 + np.exp(-x));

 def logistic_derivative(x):
     return logistic(x)*(1-logistic(x));

 class ConvolutionLayer:
     """
     卷积神经网络中的卷积层
     """
     def __init__(self,shape,padding,filters,stride):
         """
         shape 卷积层形状，元组 （行，列，通道数）
         padding 填充零个数的大小
         filters 过滤器的形状，元组 (行，列，通道数，个数)
         stride 步长
         """
         self.shape = shape;
         self.padding = padding;
         self.stride = stride;
         self.fileters = filters[:3];
         self.fileternum = filters[3];
         self.weights = [];
         for i in range(filters[3]):
             self.weights.append(np.random.randn(shape[2],filters[0],filters[1]));
         self.baises = list(np.random.randn(filters[3]));

         self.convlutionsize = (int((shape[0] + 2*padding - filters[0])/stride + 1),int((shape[1] + 2*padding - filters[1])/stride + 1));
         self.conv = np.ones((filters[3],self.convlutionsize[0],self.convlutionsize[1]));

     def Convolute(self,Data):
         """
         Data 三维数组，若只有两维则通道数设为1.
         """
         if(self.padding != 0):
             for c in range(self.shape[2]):
                 ones = np.zeros((self.shape[0]+2*self.padding,self.shape[1]+2*self.padding));
                 ones[self.padding:self.padding+self.shape[0],self.padding:self.padding+self.shape[1]] = Data[c];
                 Data[c] = ones;
         c,m,n = Data.shape;

         #遍历每一个过滤器
         for f in range(self.fileternum):
             t_conv = self.conv[f]; #取出第f个过滤器卷积后的临时容器
             w = self.weights[f]; #取出第f个过滤器的权值集合
             b = self.baises[f]; #取出第f个过滤器的偏倚
             #卷积运算，所有通道一起遍历
             row = 0;
             for i in range(self.convlutionsize[0]):
                 col = 0;
                 for j in range(self.convlutionsize[1]):
                     data = Data[:,row:row+self.fileters[0],col:col+self.fileters[1]]; #取出卷积运算的数据立方体
                     s = 0; #存放卷积立方体的乘积的和
                     #对取出的临时数据的每个通道进行卷积运算
                     for t_c in range(c):
                         t_w = w[t_c];
                         t_data = data[t_c];
                         temp = sum(np.multiply(t_w,t_data));
                         s = temp + s;
                     t_conv[i,j] = ReLU(s+b);
                     #向右移动过滤器
                     col = col + self.stride;
                 #向下移动过滤器
                 row = row + self.stride;
             #更新卷积结果容器
             self.conv[f] = t_conv;

 class PoolLayer:
     """池化层"""
     def __init__(self,shape,poolsize,stride,classic="max"):
         """
         shape 池化目标的形状， 元组（行，列，通道数）
         poolsize 池化矩阵的形状，元组 (行，列)
         stride 步长 一般情况下池化的步长等于池化大小
         classic 池化方式 max，average
         """
         self.shape = shape;
         self.stride = stride;
         self.poolsize = poolsize;
         self.classic = classic;
         #生成池化结果矩阵形状
         self.pool = np.ones((shape[2],(shape[0]-poolsize[0])/stride + 1,(shape[1]-poolsize[1])/stride + 1));
         #生成过度池化矩阵形状
         self.c_poolsize = ((shape[0]-poolsize[0])/stride + 1,(shape[1]-poolsize[1])/stride + 1);

     def Pool(self,Data):
         """
         Data 三维数组，若只有两维则通道数设为1.
         """
         c,m,n = Data.shape;

         #在每个通道上进行池化操作
         for k in range(c):
             p_temp = Data[k];
             row = 0;
             for i in range(self.c_poolsize[0]):
                 col = 0;
                 for j in range(self.c_poolsize[1]):
                     temp = p_temp[row:row+self.poolsize[0],col:col+self.poolsize[1]];
                     if(self.classic == "average"):
                         self.pool[k][i][j] = np.sum(temp) / (self.poolsize[0] * self.poolsize[1]);
                     if(self.classic == "max"):
                         self.pool[k][i][j] = np.max(temp);
                     else:
                         print("the classic does not exist");

                     col = col + self.stride;

                 row = row + self.stride;

 class FullConnectLayer:
     """全连接层"""
     def __init__(self,n_in,n_out,action_fun=logistic,action_fun_der=logistic_derivative,flag):
         """
         n_in 输入层的单元数
         n_out 输出单元个数 及紧邻下一层的单元数
         action_fun 激活函数
         action_fun_der 激活函数的导函数
         flag 初始化权值和偏倚的标记 normal，larger，smaller
         """
         self.action_fun = action_fun;
         self.action_fun_der = action_fun_der;
         self.n_in = n_in;
         self.n_out = n_out;
         init_weight_biase(flag);

     def init_weight_biase(self,init_flag):
         if(init_flag == "noraml"):
             self.weight = np.random.randn(self.n_out,self.n_in);#weight 取值服从N(0,1) 分布
             self.biase = np.random.randn(self.n_out,1);
         elif(init_flag == "larger"):
             self.weight = 2*np.random.randn(self.n_out,self.n_in)-1; #weight 取值范围（-1,1）
             self.biases = 2*np.random.randn(self.n_out,1)-1 ; #b 取值范围（-1,1）
         elif(init_flag == "smaller"):
             self.weight = np.random.randn(self.n_out,self.n_in)/np.sqrt(self.n_out) ; #weight 取值服从N(0,1/x) 分布
             self.biase = np.random.randn(self.n_out,1);

     def Forward(self,inpt):
         """全连接层的前馈传播"""
         self.inpt = np.dot(self.weight,inpt) + self.biase;
         self.outpt = self.action_fun(self.inpt);

 """Softmax Layer"""

后向传播的实现还是没有头绪，三层之间如何衔接不知道该怎么设计。本人能力水平有限，欢迎交流。本人微信号 markli52024