keras 迁移学习inception_v3，缺陷检测

from keras.models import Sequential
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.core import Activation
from keras.layers.core import Flatten
from keras.layers.core import Dropout
from keras.layers.core import Dense
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam
from keras.preprocessing import image
from keras.preprocessing.image import img_to_array
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from keras.utils import to_categorical
from keras.applications import inception_v3
from keras.layers import GlobalAveragePooling2D
from keras.models import Model
import matplotlib.pyplot as plt
import imutils
import numpy as np
import argparse
import random
import pickle
import cv2
import os
from PIL import Image
import matplotlib

matplotlib.use("Agg")

# 获取该路径下所有图片
path = list(imutils.paths.list_images(r'C:\Users\Desktop\guangdong\train'))


imagePaths = sorted(path)

random.shuffle(imagePaths)


name_dic = {'正常':'norm','不导电':'defect1','擦花':'defect2','横条压凹':'defect3','桔皮':'defect4','漏底':'defect5',
'碰伤':'defect6','起坑':'defect7','凸粉':'defect8','涂层开裂':'defect9','脏点':'defect10','其他':'defect11'}

# 将其他文件夹中，名称都改为其他
other_list_1 = os.listdir(r'C:\Users\Desktop\guangdong\train\guangdong_round1_train2_20180916\guangdong_round1_train2_20180916\瑕疵样本\其他')

other_list = other_list_1[1:]


other_dic = { '伤口':'其他', '划伤':'其他', '变形':'其他', '喷流':'其他', '喷涂碰伤':'其他', '打白点':'其他',
'打磨印':'其他','拖烂':'其他', '杂色':'其他', '气泡':'其他', '油印':'其他', '油渣':'其他',
'漆泡':'其他', '火山口':'其他', '碰凹':'其他', '粘接':'其他', '纹粗':'其他', '角位漏底':'其他',
'返底':'其他', '铝屑':'其他', '驳口':'其他'}

# 打印出name_dic里的英文部分，手动复制，再在每个后面添加‘：’及相应的数字
name_dic.values()

digit_dir = {'norm':0, 'defect1':1, 'defect2':2, 'defect3':3, 'defect4':4, 'defect5':5, 'defect6':6, 'defect7':7, 'defect8':8,
'defect9':9, 'defect10':10, 'defect11':11}

# 将图片resize成inception_v3需要的(299,299)大小，并转化成array
labels = []

data =[]
for imagePath in imagePaths:

img = Image.open(imagePath)

img = img.resize((299,299))

img = img_to_array(img)

data.append(img)

label_gbk = imagePath.split('\\')[-1].split('2')[0]
if label_gbk in other_list:

label_gbk = other_dic[label_gbk]

label_english = name_dic[label_gbk]

label = digit_dir[label_english]
print(label_gbk,':',label_english,':',label)

labels.append(label)

# 像素归一化（有利于加速收敛）
labels = np.array(labels)

data = np.array(data, dtype="float") / 255.0
# 标签one-hot
labels = to_categorical(labels)


x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)
# 数据增强
train_aug = ImageDataGenerator(rotation_range=25, width_shift_range=0.1,height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,
horizontal_flip=True, fill_mode="nearest",preprocessing_function=inception_v3.preprocess_input)
# inception_v3基础模型，include_top=False就是要修改原模型的最后一层
base_model = inception_v3.InceptionV3(weights='imagenet',include_top=False)


x = base_model.output

x = GlobalAveragePooling2D()(x)

x = Dense(units=1024,activation='relu')(x)

predictions = Dense(units=12,activation='softmax')(x)

model = Model(inputs=base_model.input, output=predictions)


base_model.summary()

model.summary()

# 不训练基础层
for layer in base_model.layers:

layer.trainable = False


model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])

# batch_size最好选2的n次方，参考的是内存格式
history_tl = model.fit_generator(generator=train_aug.flow(x=x_train,y=y_train,batch_size=32),validation_data=(x_test, y_test),
steps_per_epoch=len(x_train)//32,epochs=10,verbose=1)



model.save()