2020中国华录杯·数据湖算法大赛—定向算法赛（吸烟打电话检测)-tensorflow2.3-python3.6-迁移学习

学习一下迁移学习。。。

1.inceptionV3

"""
python=3.6
tf=2.3.1
"""
import os
import tensorflow as tf
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.applications import *
from tensorflow.keras.preprocessing.image import *
from tensorflow import keras as keras
import numpy as np
# import cv2
from  PIL import Image
import matplotlib.pyplot as plt

#2.加载数据
train_normal = r"C:\Users\Administrator\PycharmProjects\pythonProject\01_tf2_somking_calling\data\train\normal"
train_phone = r"C:\Users\Administrator\PycharmProjects\pythonProject\01_tf2_somking_calling\data\train\calling"
train_smoke = r"C:\Users\Administrator\PycharmProjects\pythonProject\01_tf2_somking_calling\data\train\smoking"

train_dir = r"C:\Users\Administrator\PycharmProjects\pythonProject\01_tf2_somking_calling\data\train"

val_dir = r"C:\Users\Administrator\PycharmProjects\pythonProject\01_tf2_somking_calling\data\val"#验证集用01_train_test_splict.py生成
val_normal = r"C:\Users\Administrator\PycharmProjects\pythonProject\01_tf2_somking_calling\data\val\normal"
val_phone = r"C:\Users\Administrator\PycharmProjects\pythonProject\01_tf2_somking_calling\data\val\calling"
val_smoke = r"C:\Users\Administrator\PycharmProjects\pythonProject\01_tf2_somking_calling\data\val\smoking"

test_dir = r"C:\Users\Administrator\PycharmProjects\pythonProject\01_tf2_somking_calling\data\test"#测试集是需要我们预测结果

train_normal_num = len(os.listdir(train_normal))
train_phone_num = len(os.listdir(train_phone))
train_smoke_num = len(os.listdir(train_smoke))

val_normal_num = len(os.listdir(val_normal))
val_phone_num = len(os.listdir(val_phone))
val_smoke_num = len(os.listdir(val_smoke))

train_all = train_normal_num + train_phone_num + train_smoke_num
val_all = val_normal_num + val_phone_num + val_smoke_num

test_num = len(os.listdir(test_dir))

print("train normal number: ", train_normal_num)
print("train phone_num: ", train_phone_num)
print("train_smoke_num: ", train_smoke_num)
print("all train images: ", train_all)

print("val normal number: ", val_normal_num)
print("val phone_num: ", val_phone_num)
print("val_smoke_num: ", val_smoke_num)
print("all val images: ", val_all)

print("all test images: ", test_num)

# 3.设置超参数
batch_size = 512        #显存内存允许的情况下尽量大
epochs = 1000            #有早停技术，尽量大

height = 299
width = 299
num_classes = 3

# 4.数据增强
# 读取训练数据并作数据增强
train_datagen = tf.keras