VGG-13 16 19网络的tensorflow.keras实现

注意几个点：

• 1.所用的数据集是从ImageNet上下载的，但数据量比较小，VGG网络是非常庞大的，因此肯定是不够去训练的，当然想要训练网络可以通过迁移学习的方法。
• 2.所有的知识点和难点都会在注释中详细说明。
• 3.整个代码一共有三个模块，基本上除了模型定义，剩下的部分和之前的AlexNet网络代码大体类似。

模型脚本：

from tensorflow.keras import layers, models, Model, Sequential

#定义分类网络结构 即最后的全连接层
def VGG(feature, im_height=224, im_width=224, class_num=1000):#feature是提取特征的网络结构
    # tensorflow中的tensor通道排序是NHWC
    input_image = layers.Input(shape=(im_height, im_width, 3), dtype="float32")
    x = feature(input_image)#提取特征得到输出
    x = layers.Flatten()(x)#展平处理
    x = layers.Dropout(rate=0.5)(x)#加一个dropout方法 减小过拟合
    x = layers.Dense(2048, activation='relu')(x)#为了节省训练参数 设置原论文一半的节点
    x = layers.Dropout(rate=0.5)(x)
    x = layers.Dense(2048, activation='relu')(x)
    x = layers.Dense(class_num)(x)
    output = layers.Softmax()(x)
    model = models.Model(inputs=input_image, outputs=output)
    return model

#通过配置列表生成提取特征的网络结构
def features(cfg):
    feature_layers = []#用来存放层结构
    for v in cfg:#通过for循环来遍历配置列表
        if v == "M":#说明该层是最大池化层
            feature_layers.append(layers.MaxPool2D(pool_size=2, strides=2))
        else:
            conv2d = layers.Conv2D(v, kernel_size=3, padding="SAME", activation="relu")
            feature_layers.append(conv2d)
    return Sequential(feature_layers, name="feature")#name是给网络结构起的一个名字

#字典：用来存储不同配置的模型结构 键是模型的配置文件，值是列表类型，其中的数字代表卷积层卷积核的个数，M代表池化层的结构(最大池化操作)
cfgs = {
    'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}


def vgg(model_name="vgg16", im_height=224, im_width=224, class_num=1000):#实例化模型 参数一：字典的key
    try:
        cfg = cfgs[model_name]#获得值
    except:
        print("Warning: model number {} not in cfgs dict!".format(model_name))
        exit(-1)
    model = VGG(features(cfg), im_height=im_height, im_width=im_width, class_num=class_num)
    return model

model = vgg(model_name='vgg11') 

训练脚本：

from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
from model import vgg
import tensorflow as tf
import json
import os


data_root = os.path.abspath(os.path.join(os.getcwd(), "../.."))  # get data root path
image_path = data_root + "/data_set/flower_data/"  # flower data set path
train_dir = image_path + "train"
validation_dir = image_path + "val"

# create direction for saving weights
if not os.path.exists("save_weights"):
    os.makedirs("save_weights")

im_height = 224
im_width = 224
batch_size = 10
epochs = 10

# 预处理
train_image_generator = ImageDataGenerator(rescale=1. / 255,#简单的缩放
                                           horizontal_flip=True)#水平方向的随机翻转
validation_image_generator = ImageDataGenerator(rescale=1. / 255)#定义验证集生成器
#读取训练集图像文件
train_data_gen = train_image_generator.flow_from_directory(directory=train_dir,
                                                           batch_size=batch_size,
                                                           shuffle=True,
                                                           target_size=(im_height, im_width),
                                                           class_mode='categorical')
total_train = train_data_gen.n#获得训练集训练样本的个数

#字典类型，返回每个类别和其索引
class_indices = train_data_gen.class_indices

# 将key和value进行反转 得到反过来的字典 (目的：在预测的过程中通过索引直接对应到类别中)
inverse_dict = dict((val, key) for key, val in class_indices.items())
# python对象转换成json对象的一个过程，生成的是字符串。
json_str = json.dumps(inverse_dict, indent=4)
with open('class_indices.json', 'w') as json_file:#将所得到的字典写入到json文件当中
    json_file.write(json_str)
#读取验证集图像文件
val_data_gen = train_image_generator.flow_from_directory(directory=validation_dir,
                                                         batch_size=batch_size,
                                                         shuffle=True,
                                                         target_size=(im_height, im_width),
                                                         class_mode='categorical')
total_val = val_data_gen.n

model = vgg("vgg16", 224, 224, 5)#实例化网络
model.summary()

# using keras high level api for training
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False),
              metrics=["accuracy"])

callbacks = [tf.keras.callbacks.ModelCheckpoint(filepath='./save_weights/myAlex_{epoch}.h5',
                                                save_best_only=True,
                                                save_weights_only=True,
                                                monitor='val_loss')]

# tensorflow2.1 recommend to using fit
history = model.fit(x=train_data_gen,
                    steps_per_epoch=total_train // batch_size,
                    epochs=epochs,
                    validation_data=val_data_gen,
                    validation_steps=total_val // batch_size,
                    callbacks=callbacks)

预测脚本：

from model import vgg
from PIL import Image
import numpy as np
import json
import matplotlib.pyplot as plt

im_height = 224
im_width = 224

# load image
img = Image.open("../tulip.jpg")
# resize image to 224x224
img = img.resize((im_width, im_height))
plt.imshow(img)

# scaling pixel value to (0-1)
img = np.array(img) / 255.

# Add the image to a batch where it's the only member.
img = (np.expand_dims(img, 0))

# read class_indict
try:
    json_file = open('./class_indices.json', 'r')
    class_indict = json.load(json_file)
except Exception as e:
    print(e)
    exit(-1)

model = vgg("vgg16", 224, 224, 5)
model.load_weights("./save_weights/myVGG.h5")
result = np.squeeze(model.predict(img))
predict_class = np.argmax(result)
print(class_indict[str(predict_class)], result[predict_class])
plt.show()