深度学习第6天：示例代码解析-ResNet深度残差网络

以下是一个简化的 ResNet 模型中，有以下主要组件：

• 卷积层（Conv2D）：模型开始的卷积层，用于提取图像特征。
• 最大池化层（MaxPool）：提取图像中显著的特征
• 4 个残差块（residual_block）：每个残差块包括两个卷积层。
• 全局平均池化层（GlobalAveragePooling2D）：用于将每个通道的特征平均化，产生一个固定大小的输出。
• 全连接层（Dense）：输出层，根据任务的不同可能有不同的神经元数量。

import tensorflow as tf
from tensorflow.keras import layers, Model

def residual_block(x, filters, kernel_size=3, stride=1, conv_shortcut=False):
    shortcut = x

    if conv_shortcut:
        shortcut = layers.Conv2D(filters, kernel_size=1, strides=stride, padding='same')(shortcut)
        shortcut = layers.BatchNormalization()(shortcut)

    x = layers.Conv2D(filters, kernel_size, strides=stride, padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)

    x = layers.Conv2D(filters, kernel_size, padding='same')(x)
    x = layers.BatchNormalization()(x)

    x = layers.add([x, shortcut])
    x = layers.Activation('relu')(x)

    return x

def resnet(input_shape, num_classes=10):
    inputs = tf.keras.Input(shape=input_shape)

    x = layers.Conv2D(64, 7, strides=2, padding='same')(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.MaxPooling2D(3, strides=2, padding='same')(x)

    x = residual_block(x, 64)
    x = residual_block(x, 64)
    x = residual_block(x, 128, stride=2)
    x = residual_block(x, 128)
    x = residual_block(x, 256, stride=2)
    x = residual_block(x, 256)
    x = residual_block(x, 512, stride=2)
    x = residual_block(x, 512)

    x = layers.GlobalAveragePooling2D()(x)
    x = layers.Dense(num_classes, activation='softmax')(x)

    model = Model(inputs, x)

    return model

# 创建ResNet模型
model = resnet(input_shape=(224, 224, 3), num_classes=1000)

# 打印模型概要
model.summary()