python实现的卷积神经网络CNN,无框架

#! /usr/bin/env python # -*- coding: utf-8 -*- from numpy import * import numpy as np import matplotlib.pyplot as plt import matplotlib.image as mgimg import math import gParam import copy import scipy.signal as signal # createst uniform random array w/ values in [a,b) and shape args # return value type is ndarray def rand_arr(a, b, *args): np.random.seed(0) return np.random.rand(*args) * (b - a) + a # Class Cnn class Ccnn: def __init__(self, cLyNum, pLyNum,fLyNum,oLyNum): self.cLyNum = cLyNum self.pLyNum = pLyNum self.fLyNum = fLyNum self.oLyNum = oLyNum self.pSize = gParam.P_SIZE self.yita = 0.01 self.cLyBias = rand_arr(-0.1, 0.1, 1,cLyNum) self.fLyBias = rand_arr(-0.1, 0.1, 1,fLyNum) self.kernel_c = zeros((gParam.C_SIZE,gParam.C_SIZE,cLyNum)) self.kernel_f = zeros((gParam.F_NUM,gParam.F_NUM,fLyNum)) for i in range(cLyNum): self.kernel_c[:,:,i] = rand_arr(-0.1,0.1,gParam.C_SIZE,gParam.C_SIZE) for i in range(fLyNum): self.kernel_f[:,:,i] = rand_arr(-0.1,0.1,gParam.F_NUM,gParam.F_NUM) self.pooling_a = ones((self.pSize,self.pSize))/(self.pSize**2) self.weight_f = rand_arr(-0.1,0.1, pLyNum, fLyNum) self.weight_output = rand_arr(-0.1,0.1,fLyNum,oLyNum) def read_pic_data(self, path, i): #print 'read_pic_data' data = np.array([]) full_path = path + '%d'%i + gParam.FILE_TYPE try: data = mgimg.imread(full_path) #data is np.array data = (double)(data) except IOError: raise Exception('open file error in read_pic_data():', full_path) return data def read_label(self, path): #print 'read_label' ylab = [] try: fobj = open(path, 'r') for line in fobj: ylab.append(line.strip()) fobj.close() except IOError: raise Exception('open file error in read_label():', path) return ylab #卷积层 def convolution(self, data, kernel): data_row, data_col = shape(data) kernel_row, kernel_col = shape(kernel) n = data_col - kernel_col m = data_row - kernel_row state = zeros((m+1, n+1)) for i in range(m+1): for j in range(n+1): temp = multiply(data[i:i+kernel_row,j:j+kernel_col], kernel) state[i][j] = temp.sum() return state # 池化层 def pooling(self, data, pooling_a): data_r, data_c = shape(data) p_r, p_c = shape(pooling_a) r0 = data_r/p_r c0 = data_c/p_c state = zeros((r0,c0)) for i in range(c0): for j in range(r0): temp = multiply(data[p_r*i:p_r*i+1,p_c*j:p_c*j+1],pooling_a) state[i][j] = temp.sum() return state #全连接层 def convolution_f1(self, state_p1, kernel_f1, weight_f1): #池化层出来的20个特征矩阵乘以池化层与全连接层的连接权重进行相加 #wx(这里的偏置项=0),这个结果然后再和全连接层中的神经元的核 #进行卷积,返回值: #1：全连接层卷积前,只和weight_f1相加之后的矩阵 #2：和全连接层卷积完之后的矩阵 n_p0, n_f = shape(weight_f1)#n_p0=20(是Feature Map的个数);n_f是100(全连接层神经元个数) m_p, n_p, pCnt = shape(state_p1)#这个矩阵是三维的 m_k_f1, n_k_f1,fCnt = shape(kernel_f1)#12*12*100 state_f1_temp = zeros((m_p,n_p,n_f)) state_f1 = zeros((m_p - m_k_f1 + 1,n_p - n_k_f1 + 1,n_f)) for n in range(n_f): count = 0 for m in range(n_p0): temp = state_p1[:,:,m] * weight_f1[m][n] count = count + temp state_f1_temp[:,:,n] = count state_f1[:,:,n] = self.convolution(state_f1_temp[:,:,n], kernel_f1[:,:,n]) return state_f1, state_f1_temp # softmax 层 def softmax_layer(self,state_f1): # print 'softmax_layer' output = zeros((1,self.oLyNum)) t1 = (exp(np.dot(state_f1,self.weight_output))).sum() for i in range(self.oLyNum): t0 = exp(np.dot(state_f1,self.weight_output[:,i])) output[:,i]=t0/t1 return output #误差反向传播更新权值 def cnn_upweight(self,err_cost, ylab, train_data,state_c1, \ state_s1, state_f1, state_f1_temp, output): #print 'cnn_upweight' m_data, n_data = shape(train_data) # softmax的资料请查看 (TODO) label = zeros((1,self.oLyNum)) label[:,ylab] = 1 delta_layer_output = output - label weight_output_temp = copy.deepcopy(self.weight_output) delta_weight_output_temp = zeros((self.fLyNum, self.oLyNum)) #print shape(state_f1) #更新weight_output for n in range(self.oLyNum): delta_weight_output_temp[:,n] = delta_layer_output[:,n] * state_f1 weight_output_temp = weight_output_temp - self.yita * delta_weight_output_temp #更新bais_f和kernel_f (推导公式请查看 TODO) delta_layer_f1 = zeros((1, self.fLyNum)) delta_bias_f1 = zeros((1,self.fLyNum)) delta_kernel_f1_temp = zeros(shape(state_f1_temp)) kernel_f_temp = copy.deepcopy(self.kernel_f) for n in range(self.fLyNum): count = 0 for m in range(self.oLyNum): count = count + delta_layer_output[:,m] * self.weight_output[n,m] delta_layer_f1[:,n] = np.dot(count, (1 - np.tanh(state_f1[:,n])**2)) delta_bias_f1[:,n] = delta_layer_f1[:,n] delta_kernel_f1_temp[:,:,n] = delta_layer_f1[:,n] * state_f1_temp[:,:,n] # 1 self.fLyBias = self.fLyBias - self.yita * delta_bias_f1 kernel_f_temp = kernel_f_temp - self.yita * delta_kernel_f1_temp #更新weight_f1 delta_layer_f1_temp = zeros((gParam.F_NUM,gParam.F_NUM,self.fLyNum)) delta_weight_f1_temp = zeros(shape(self.weight_f)) weight_f1_temp = copy.deepcopy(self.weight_f) for n in range(self.fLyNum): delta_layer_f1_temp[:,:,n] = delta_layer_f1[:,n] * self.kernel_f[:,:,n] for n in range(self.pLyNum): for m in range(self.fLyNum): temp = delta_layer_f1_temp[:,:,m] * state_s1[:,:,n] delta_weight_f1_temp[n,m] = temp.sum() weight_f1_temp = weight_f1_temp - self.yita * delta_weight_f1_temp # 更新bias_c1 n_delta_c = m_data - gParam.C_SIZE + 1 delta_layer_p = zeros((gParam.F_NUM,gParam.F_NUM,self.pLyNum)) delta_layer_c = zeros((n_delta_c,n_delta_c,self.pLyNum)) delta_bias_c = zeros((1,self.cLyNum)) for n in range(self.pLyNum): count = 0 for m in range(self.fLyNum): count = count + delta_layer_f1_temp[:,:,m] * self.weight_f[n,m] delta_layer_p[:,:,n] = count #print shape(np.kron(delta_layer_p[:,:,n], ones((2,2))/4)) delta_layer_c[:,:,n] = np.kron(delta_layer_p[:,:,n], ones((2,2))/4) \ * (1 - np.tanh(state_c1[:,:,n])**2) delta_bias_c[:,n] = delta_layer_c[:,:,n].sum() # 2 self.cLyBias = self.cLyBias - self.yita * delta_bias_c #更新 kernel_c1 delta_kernel_c1_temp = zeros(shape(self.kernel_c)) for n in range(self.cLyNum): temp = delta_layer_c[:,:,n] r1 = map(list,zip(*temp[::1]))#逆时针旋转90度 r2 = map(list,zip(*r1[::1]))#再逆时针旋转90度 temp = signal.convolve2d(train_data, r2,'valid') temp1 = map(list,zip(*temp[::1])) delta_kernel_c1_temp[:,:,n] = map(list,zip(*temp1[::1])) self.kernel_c = self.kernel_c - self.yita * delta_kernel_c1_temp self.weight_f = weight_f1_temp self.kernel_f = kernel_f_temp self.weight_output = weight_output_temp # predict def cnn_predict(self,data): return