TensorFlow2实战-系列教程14：Resnet实战2

🧡💛💚TensorFlow2实战-系列教程总目录

有任何问题欢迎在下面留言
本篇文章的代码运行界面均在Jupyter Notebook中进行
本篇文章配套的代码资源已经上传

Resnet实战1
Resnet实战2
Resnet实战3

4、训练脚本train.py解读------创建模型

def get_model():model = resnet50.ResNet50()if config.model == "resnet34":model = resnet34.ResNet34()if config.model == "resnet101":model = resnet101.ResNet101()if config.model == "resnet152":model = resnet152.ResNet152()model.build(input_shape=(None, config.image_height, config.image_width, config.channels))model.summary()tf.keras.utils.plot_model(model, to_file='model.png')return model# create model
model = get_model()

调用get_model()函数构建模型

get_model()函数：

通过resnet50.py调用ResNet50类，构建ResNet50模型
如果在配置参数中设置的是"resnet34"、“resnet101”、“resnet152”，则会对应使用（resnet34.py调用ResNet34类，构建ResNet34模型）、（resnet101.py调用ResNet101类，构建ResNet101模型）、（resnet152.py调用ResNet152类，构建ResNet152模型）
准备模型以供训练或评估,
输出模型的概览
创建了模型的结构图，plot_model 函数从 Keras 工具包中生成模型的可视化表示，指定了保存路径

5、模型构建解析------models/resnet50.py

import tensorflow as tf
from models.residual_block import build_res_block_2
from config import NUM_CLASSESclass ResNet50(tf.keras.Model):def __init__(self, num_classes=NUM_CLASSES):super(ResNet50, self).__init__()self.pre1 = tf.keras.layers.Conv2D(filters=64, kernel_size=(7, 7), strides=2, padding='same')self.pre2 = tf.keras.layers.BatchNormalization()self.pre3 = tf.keras.layers.Activation(tf.keras.activations.relu)self.pre4 = tf.keras.layers.MaxPool2D(pool_size=(3, 3), strides=2)self.layer1 = build_res_block_2(filter_num=64, blocks=3)self.layer2 = build_res_block_2(filter_num=128, blocks=4, stride=2)self.layer3 = build_res_block_2(filter_num=256, blocks=6, stride=2)self.layer4 = build_res_block_2(filter_num=512, blocks=3, stride=2)self.avgpool = tf.keras.layers.GlobalAveragePooling2D()self.fc1 = tf.keras.layers.Dense(units=1000, activation=tf.keras.activations.relu)self.drop_out = tf.keras.layers.Dropout(rate=0.5)self.fc2 = tf.keras.layers.Dense(units=num_classes, activation=tf.keras.activations.softmax)def call(self, inputs, training=None, mask=None):pre1 = self.pre1(inputs)pre2 = self.pre2(pre1, training=training)pre3 = self.pre3(pre2)pre4 = self.pre4(pre3)l1 = self.layer1(pre4, training=training)l2 = self.layer2(l1, training=training)l3 = self.layer3(l2, training=training)l4 = self.layer4(l3, training=training)avgpool = self.avgpool(l4)fc1 = self.fc1(avgpool)drop = self.drop_out(fc1)out = self.fc2(drop)return out

class ResNet50(tf.keras.Model)，这个类定义了ResNet50模型的结构，以及前向传播的方式、顺序

ResNet50类解析：

构造函数，传入了预测的类别数
初始化
pre1 ，定义一个二维卷积，输出64个特征图，7x7的卷积，步长为2
pre2 ，定义一个批归一化
pre3，定义一个ReLU激活函数
pre4，一个二维的最大池化
依次通过build_res_block_2()函数定义4个残差块
定义一个全局平均池化
定义一个全连接层，输出维度为1000
定义一个dropout
定义一个输出层的全连接层
前向传播函数，传入输入值
依次经过pre1、pre2、pre3、pre4，即卷积、批归一化、ReLU、最大池化
依次经过layer1 、layer2 、layer3 、layer4 等四个残差块
将layer4 的输出经过平局池化
依次经过两个全连接层

6、模型构建解析------models/residual_block.py

BottleNeck类
build_res_block_2()函数
build_res_block_2()函数通过调用BottleNeck类构建残差块

class BottleNeck(tf.keras.layers.Layer):def __init__(self, filter_num, stride=1,with_downsample=True):super(BottleNeck, self).__init__()self.with_downsample = with_downsampleself.conv1 = tf.keras.layers.Conv2D(filters=filter_num, kernel_size=(1, 1), strides=1, padding='same')self.bn1 = tf.keras.layers.BatchNormalization()self.conv2 = tf.keras.layers.Conv2D(filters=filter_num, kernel_size=(3, 3), strides=stride, padding='same')self.bn2 = tf.keras.layers.BatchNormalization()self.conv3 = tf.keras.layers.Conv2D(filters=filter_num * 4, kernel_size=(1, 1), strides=1, padding='same')self.bn3 = tf.keras.layers.BatchNormalization()self.downsample = tf.keras.Sequential()self.downsample.add(tf.keras.layers.Conv2D(filters=filter_num * 4, kernel_size=(1, 1), strides=stride))self.downsample.add(tf.keras.layers.BatchNormalization())def call(self, inputs, training=None):identity = self.downsample(inputs)conv1 = self.conv1(inputs)bn1 = self.bn1(conv1, training=training)relu1 = tf.nn.relu(bn1)conv2 = self.conv2(relu1)bn2 = self.bn2(conv2, training=training)relu2 = tf.nn.relu(bn2)conv3 = self.conv3(relu2)bn3 = self.bn3(conv3, training=training)if self.with_downsample == True:output = tf.nn.relu(tf.keras.layers.add([identity, bn3]))else:output = tf.nn.relu(tf.keras.layers.add([inputs, bn3]))return output

BottleNeck类解析：

继承tf.keras.layers.Layer
构造函数，传入特征图个数、步长、是否下采样等参数
初始化
是否进行下采样参数
定义一个1x1，步长为1的二维卷积conv1
conv1 对应的批归一化
定义一个3x3，步长为1的二维卷积conv2
conv2 对应的批归一化
定义一个3x3，步长为1的二维卷积conv2
conv3 对应的批归一化
定义一个下采样层（self.downsample），这个层是一个包含卷积层和批量归一化的 Sequential 模型，用于匹配输入和残差的维度
call()函数为前向传播
应用下采样
应用三层卷积和批量归一化以及对应的ReLU
with_downsample == True：
启用下采样，将下采样后的输入（identity）与最后一个卷积层的输出（bn3）相加
没有启用下采样，将原始输入（inputs）与最后一个卷积层的输出（bn3）相加

def build_res_block_2(filter_num, blocks, stride=1):res_block = tf.keras.Sequential()res_block.add(BottleNeck(filter_num, stride=stride))for _ in range(1, blocks):res_block.add(BottleNeck(filter_num, stride=1,with_downsample=False))    return res_block