深度学习入门项目完整流程——图片制作数据集、训练网络、测试准确率（TensorFlow+keras）

首先将训练的图片和标签制作成数据集，我用的是numpy库里的savez函数，可以将numpy数组保存为.npz文件（无压缩，所以文件较大）。

import cv2 as cv
import numpy as np
import os
import glob

#调整图像的大小、制作数据集
def img_process(subpath,path_list):
	print('This is',subpath,'dataset.')
	img=np.zeros((len(path_list),1145,1145),dtype=np.uint8)
	if subpath=='pos':
		label=np.zeros(len(path_list),dtype=np.uint8)
	else:
		label=np.ones(len(path_list),dtype=np.uint8)
	finish=0;
	for img_path in path_list:
		#读取灰度图
		aimg = cv.imread(img_path,cv.IMREAD_GRAYSCALE) 
		#print('This image shape:',img.shape) 
		aimg=cv.resize(aimg,(1145,1145))
		#aimg=aimg.reshape(1,-1)
		img[finish]=aimg
		finish+=1
		if finish%100==0:
			print('...',finish,'/',len(path_list))
	print(img.shape)
	#print(label.shape)
	return (img,label)
	

def make_npz(img,label,wpath):
	np.savez(wpath,trainx=img,trainy=label)
	
path=""
wpath=""
path_list=[]
path_list.extend(glob.glob(path+'pos/*.jpg'))
path_list2=[]
path_list2.extend(glob.glob(path+'neg/*.jpg'))

for i in range(20):
	sublist=path_list[i*500:i*500+500]
	#print(i,len(sublist),i*500)
	i1,l1=img_process('pos',sublist)
	i2,l2=img_process('neg',path_list2)
	img=np.zeros((len(sublist)*2,1145,1145),dtype=np.uint8)
	label=np.zeros((len(sublist)*2),dtype=np.uint8)
	for j in range(len(path_list2)):
		img[j*2]=i1[j]
		label[j*2]=l1[j]
		img[j*2+1]=i2[j]
		label[j*2+1]=l2[j]
	md_name='c%d.npz'%i
	make_npz(img,label,wpath+md_name)

训练网络选择了alexnet，注意要根据图片大小调整网络的输入，根据分类个数调整网络输出。训练前需要把输入数据转化为float类型。因为没有测试集标注，所以训练时把训练集作为测试集输入（也可以不输入）。训练过程中出现了loss突然增大的现象，暂时还没搞明白原因。

#coding=utf-8 
import keras 
from keras import backend as K
from keras.models import Sequential  
from keras.layers import Dense,Flatten,Dropout  
from keras.layers.convolutional import Conv2D,MaxPooling2D  
from keras.utils.np_utils import to_categorical  
import numpy as np  
from keras.callbacks import TensorBoard
import os

#os.environ["CUDA_VISIBLE_DEVICES"] = "4,5"

seed = 7  
np.random.seed(seed) 
num_classes=2
img_rows=1145
img_cols=1145
path=''

model = Sequential()
model.add(Conv2D(3,(15,15),strides=(5,5),input_shape=(1145,1145,1),padding='valid',activation='relu',kernel_initializer='uniform'))  #新加层
model.add(Conv2D(96,(11,11),strides=(4,4),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(MaxPooling2D(pool_size=(3,3),strides=(2,2)))  
model.add(Conv2D(256,(5,5),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(MaxPooling2D(pool_size=(3,3),strides=(2,2)))  
model.add(Conv2D(384,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(Conv2D(384,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(Conv2D(256,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(MaxPooling2D(pool_size=(3,3),strides=(2,2)))  
model.add(Flatten())  
model.add(Dense(4096,activation='relu'))  
model.add(Dropout(0.5))  
model.add(Dense(4096,activation='relu'))  
model.add(Dropout(0.5))  
model.add(Dense(1000,activation='relu'))
model.add(Dense(num_classes,activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])  
model.summary()

for i in range(20):
	md_name='c%d.npz'%i
	t=np.load(path+md_name);
	img=t['trainx']
	label=t['trainy']
	if K.image_data_format() == 'channels_first':
		img=img.reshape(img.shape[0],1,img_rows,img_cols)
	else:
		img=img.reshape(img.shape[0],img_rows,img_cols,1)
	img=img.astype('float32')
	img/=255.0
	label=keras.utils.to_categorical(label,num_classes)
	print('>>>>>>>>>begin...  i=',i+1,'shape =',img.shape,label.shape)
	#开始训练
	model.fit(img,label,batch_size=128,epochs=20,validation_data=(img,label))
	score=model.evaluate(img,label)
	print('         Test loss:',score[0])
	print('         Test accuracy:',score[1])

测试只需要加载训练模型，调用predict函数即可完成，注意输出的结果表示为one-hot类型，需要转化。

from keras.models import load_model
from keras import backend as K
import cv2 as cv
import numpy as np
import os
import glob

img_rows=1145
img_cols=1145
num_classes=2

#调整图像的大小、制作数据集
def img_process(path_list):
	img=np.zeros((len(path_list),1145,1145),dtype=np.uint8)
	finish=0;
	for img_path in path_list:
		#读取灰度图
		aimg=cv.imread(img_path,cv.IMREAD_GRAYSCALE)
		aimg=cv.resize(aimg,(1145,1145))
		img[finish]=aimg
		finish+=1
		if finish%100==0:
			print('...',finish,'/',len(path_list))
	return img

path=' '
path_list=[]
for i in range(2000):
	path_list.append(path+'%d.jpg'%(i+1))
img=img_process(path_list)
model=load_model('model.h5')
if K.image_data_format() == 'channels_first':
	img = img.reshape(img.shape[0], 1, img_rows, img_cols)
else:
	img = img.reshape(img.shape[0], img_rows, img_cols, 1)
print('>>>>>>>>>>>>Read image finished!>>>>>>>>>>>>>')
pre=model.predict(img)
print(pre.shape,type(pre[0][0]))
f=open('op.txt','w')
f2=open('op2.txt','w')
for i in range(img.shape[0]):
	f.write("%d.jpg %d\r"%(i+1,1-np.argmax(pre[i])))
	f2.write("%d.jpg   %0.3f  %0.3f \r" % (i+1,pre[i][0],pre[i][1]))

继续改进中……