前言

CNN算法方面主要参考的的zh_JNU同学的工作和Deep-Learning-ToolBox-CNN-master的Matlab源码，然后也做了些修改和解读。

Keras

数据库是5钟分类的400张训练数据和100张测试数据，数据库网盘（提取码：f5ze）可能跟环境版本有关，我这边的预处理不能使用cv的方法，所以统一使用cv2里的方法，值得强调的是，Keras版本亦有差异，我这边的版本大致如图
在这里插入图片描述
首先是预处理，利用opencv对图片进行缩放和灰化，将RGB3通道降维到单通道，python果然是“玩弄”字符串的语言

#coding:utf8
import os
import cv2# 遍历指定目录，显示目录下的所有文件名
#width_scale = 192 #缩放尺寸宽度
#height_scale = 128#缩放尺寸高度
write_path = "/data_base/all_data/test_scale/"#要写入的图片路径"/data_base/all_data/train_scale/"#遍历每一张图片进行处理
def eachFile(filepath):pathDir = os.listdir(filepath)for allDir in pathDir:child = os.path.join('%s%s' % (filepath,allDir))write_child = os.path.join('%s%s' % (write_path,allDir))image = cv2.imread(child,cv2.IMREAD_GRAYSCALE)#image = cv.LoadImage(child,0)shrink = cv2.resize(image,(126,126),interpolation=cv2.INTER_AREA) #(192,128)cv2.imwrite(write_child,shrink)
#    break
if __name__ == '__main__':filePathC = "/data_base/all_data/test/"##"/data_base/all_data/train/" 训练测试数据集分别处理eachFile(filePathC)

这里的预处理后的维度调整为126*126，主要考虑的是每层池化层的输入都为偶数，卷积核kernel size 为3，那么，输入层126*126 --> 1卷积层124*124–>1池化层62*62–>2卷积层60*60–>2池化层30*30–>3卷积层28*28–>3池化层14*14，接下来就是全连接，预处理后
在这里插入图片描述

然后是测试，包括网络的训练和测试，我这里除了版本兼容上的调整，也做了些改动，
首先是修改后的源码，这里有一些术语，我将它们梳理一下

1外层循环

epoch：完整样本训练的次数；
batch：1个样本空间中样本的个数；
有时1个完整的样本空间可能包含大量的样本，这时需要对样本进行分割，分成minibatch，那么完成一次样本训练就需要迭代iteration = batch/minibatch；minibatch的一种极限情形就是等于1，理论上不是不可以，但是容易过拟合。

2中部循环

filters：某一个卷积层中卷积核的个数，之后再经过池化层后，每一个filter都会对应一个feature map（特征图）

3内部循环

kernel_size：卷积核的维数，在滑动过程中也会有stride、padding等参数的选取
label:标签的维数，其实我觉得标签的选取还是挺自由的，可以认为每个分类就是一个实数，也可以是种类维度的独热码向量，如果是1*1的实数R，在BP过程可能会简单点，因为均方误差也是一个实数，独热码向量的形式，其实已经被赋予了一定的意义，即每一位代表了样本为该类的可能性（probability）

#coding:utf8
import re
import cv2
import os
import numpy as npfrom keras.models import Sequential
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.core import Dense,Dropout,Activation,Flatten
from keras.layers.convolutional import Conv2D,MaxPooling2D
from keras.optimizers import Adam
from keras.utils import np_utils#得到一共多少个样本
def getnum(file_path):pathDir = os.listdir(file_path)i = 0for allDir in pathDir:i +=1return i
#制作数据集
def data_label(path,count):data = np.empty((count,1,126,126),dtype = 'float32')#建立空的四维张量类型32位浮点label = np.empty((count,),dtype = 'uint8')i = 0pathDir = os.listdir(path)for each_image in pathDir:all_path = os.path.join('%s%s' % (path,each_image))#路径进行连接image = cv2.imread(all_path,0)mul_num = re.findall(r"\d",all_path)#寻找字符串中的数字，由于图像命名为300.jpg 标签设置为0num = int(mul_num[0])-3
#        print num,each_image
#        cv2.imshow("fad",image)
#        print childarray = np.asarray(image,dtype='float32')array -= np.min(array)array /= np.max(array)data[i,:,:,:] = arraylabel[i] = int(num)i += 1return data,label
#构建卷积神经网络
def cnn_model(train_data,train_label,test_data,test_label):model = Sequential()
#卷积层 12 × 124 × 124 大小model.add(Conv2D(filters = 12,kernel_size = (3,3),padding = "valid",data_format = "channels_first",input_shape = (1,126,126)))model.add(Activation('relu'))#激活函数使用修正线性单元
#池化层12 × 62 × 62model.add(MaxPooling2D(pool_size = (2,2),strides = (2,2),padding = "valid"))
#卷积层 24 * 60 * 60model.add(Conv2D(24,(3,3),padding = "valid",data_format = "channels_first"))model.add(Activation('relu'))
#池化层 24×30×30model.add(MaxPooling2D(pool_size = (2,2),strides = (2,2),padding = "valid"))
#卷积层	48×28×28model.add(Conv2D(48,(3,3),padding = "valid",data_format = "channels_first"))model.add(Activation('relu'))
#池化层	48×14×14model.add(MaxPooling2D(pool_size = (2,2),strides =(2,2),padding = "valid"))model.add(Flatten())model.add(Dense(20))model.add(Activation(LeakyReLU(0.3)))model.add(Dropout(0.5))model.add(Dense(20))model.add(Activation(LeakyReLU(0.3)))model.add(Dropout(0.4))model.add(Dense(5,kernel_initializer="normal"))model.add(Activation('softmax'))adam = Adam(lr = 0.001)model.compile(optimizer = adam,loss =  'categorical_crossentropy',metrics = ['accuracy'])print ('----------------training-----------------------')model.fit(train_data,train_label,batch_size = 20,epochs = 50,shuffle = "True",validation_split = 0.1)print ('----------------testing------------------------')loss,accuracy = model.evaluate(test_data,test_label)print ('\n test loss:',loss)print ('\n test accuracy',accuracy)
train_path = "/data_base/all_data/train_scale/"
test_path = "/data_base/all_data/test_scale/"
train_count = getnum(train_path)
test_count = getnum(test_path)
train_data,train_label = data_label(train_path,train_count)
test_data,test_label = data_label(test_path,test_count)
train_label = np_utils.to_categorical(train_label,num_classes = 5)
test_label = np_utils.to_categorical(test_label,num_classes = 5)
cnn_model(train_data,train_label,test_data,test_label)#print getnum('/home/zhanghao/data/classification/test_scale/')
#data_label('/home/zhanghao/data/classification/test_scale/',1)
#cv.WaitKey(0)

在这里插入图片描述
代码是3层卷积+池化再进行全连接，因为kernel_size是33，并且没有对输入样本扩展，所以卷积层后的维数就是Nin-kernel_size+1，池化层是22，所以池化后宽高减半。

查看中间变量的方法，插入观察层，首先要给每一层都取个名字，目前，它的这个打印结果有点令人挠头

#coding:utf8
import re
import cv2
import os
import numpy as npfrom keras.models import Sequential
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.core import Dense,Dropout,Activation,Flatten
from keras.layers.convolutional import Conv2D,MaxPooling2D
from keras.optimizers import Adam
from keras.utils import np_utils
from keras.layers import Densefrom keras.models import Model #得到一共多少个样本
def getnum(file_path):pathDir = os.listdir(file_path)i = 0for allDir in pathDir:i +=1return i
#制作数据集
def data_label(path,count):data = np.empty((count,1,126,126),dtype = 'float32')#建立空的四维张量类型32位浮点label = np.empty((count,),dtype = 'uint8')i = 0pathDir = os.listdir(path)for each_image in pathDir:all_path = os.path.join('%s%s' % (path,each_image))#路径进行连接image = cv2.imread(all_path,0)mul_num = re.findall(r"\d",all_path)#寻找字符串中的数字，由于图像命名为300.jpg 标签设置为0num = int(mul_num[0])-3
#        print num,each_image
#        cv2.imshow("fad",image)
#        print childarray = np.asarray(image,dtype='float32')array -= np.min(array)array /= np.max(array)data[i,:,:,:] = arraylabel[i] = int(num)i += 1return data,label
#构建卷积神经网络
def cnn_model(train_data,train_label,test_data,test_label):model = Sequential()
#卷积层 12 × 120 × 120 大小model.add(Conv2D(filters = 12,kernel_size = (3,3),padding = "valid",data_format = "channels_first",input_shape = (1,126,126),name="cnn1"))model.add(Activation('relu'))#激活函数使用修正线性单元
#池化层12 × 60 × 60model.add(MaxPooling2D(pool_size = (2,2),strides = (2,2),padding = "valid",name="mxp1"))
#卷积层 24 * 58 * 58model.add(Conv2D(24,(3,3),padding = "valid",data_format = "channels_first",name="cnn2"))model.add(Activation('relu'))
#池化层 24×29×29model.add(MaxPooling2D(pool_size = (2,2),strides = (2,2),padding = "valid",name="mxp2"))
#卷积层		model.add(Conv2D(48,(3,3),padding = "valid",data_format = "channels_first",name="cnn3"))model.add(Activation('relu'))
#池化层	model.add(MaxPooling2D(pool_size = (2,2),strides =(2,2),padding = "valid",name="mxp3"))model.add(Flatten())model.add(Dense(20))model.add(Activation(LeakyReLU(0.3)))model.add(Dropout(0.5))model.add(Dense(20))model.add(Activation(LeakyReLU(0.3)))model.add(Dropout(0.4))model.add(Dense(5,kernel_initializer="normal"))model.add(Activation('softmax'))adam = Adam(lr = 0.001)model.compile(optimizer = adam,loss =  'categorical_crossentropy',metrics = ['accuracy'])print ('----------------training-----------------------')model.fit(train_data,train_label,batch_size = 20,epochs = 1,shuffle = "True",validation_split = 0.1)print ('----------------testing------------------------')loss,accuracy = model.evaluate(test_data,test_label)print ('\n test loss:',loss)print ('\n test accuracy',accuracy)##注意缩进 观察层应为模型之中dense1_layer_model = Model(inputs=model.input,outputs=model.get_layer("cnn2").output)dense1_output = dense1_layer_model.predict(train_data)print (dense1_output.shape)weight_Dense_1,bias_Dense_1 = model.get_layer("cnn2").get_weights()	print(weight_Dense_1.shape)print(bias_Dense_1.shape)print(weight_Dense_1)print(bias_Dense_1)train_path = "/data_base/all_data/train_scale/"
test_path = "/data_base/all_data/test_scale/"
train_count = getnum(train_path)
test_count = getnum(test_path)
train_data,train_label = data_label(train_path,train_count)
test_data,test_label = data_label(test_path,test_count)
train_label = np_utils.to_categorical(train_label,num_classes = 5)
test_label = np_utils.to_categorical(test_label,num_classes = 5)
cnn_model(train_data,train_label,test_data,test_label)##错误
#取某一层的输出为输出新建为model，采用函数模型
#dense1_layer_model = Model(inputs=model.input,
#									outputs=model.get_layer('cnn1').output)
#以这个model的预测值作为输出
#dense1_output = dense1_layer_model.predict(data)#print getnum('/home/zhanghao/data/classification/test_scale/')
#data_label('/home/zhanghao/data/classification/test_scale/',1)
#cv.WaitKey(0)train_path = "/data_base/all_data/train_scale/"
test_path = "/data_base/all_data/test_scale/"
train_count = getnum(train_path)
test_count = getnum(test_path)
train_data,train_label = data_label(train_path,train_count)
test_data,test_label = data_label(test_path,test_count)
train_label = np_utils.to_categorical(train_label,num_classes = 5)
test_label = np_utils.to_categorical(test_label,num_classes = 5)
cnn_model(train_data,train_label,test_data,test_label)

在这里插入图片描述

Matlab CNN ToolBox

这个工具箱最早是在Github上开源的，论坛上也有很多同学对源码进行解读，我觉得都挺好的，不过，我觉得最好的辅助理解是Sunshine同学写的两层CNN的Matlab实现，他把前一个卷积+池化和后一个卷积+池化之间的前馈和反馈连接关系讲得很清楚，并且，和这个CNN ToolBox的处理方式一致，读者能够很直观地看到各个子层的维数变化