ResNet网络结构

ResNet是一种深度卷积神经网络，它的主要特点是使用了残差学习模块，可以有效地解决深度网络训练过程中的梯度消失和梯度爆炸问题。ResNet的网络结构由多个ResNet Block组成，每个ResNet Block包含多个卷积层和一个跳跃连接。跳跃连接将输入直接加到输出上，使得网络可以学习残差，从而更好地适应训练数据。

以下是ResNet50的网络结构示意图：

# 输入层
input_layer = Input(shape=(224, 224, 3))

# 第一层卷积
x = Conv2D(filters=64, kernel_size=(7, 7), strides=(2, 2), padding='same')(input_layer)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

# conv2_x
x = resnet_block(x, filters=[64, 64, 256], s=1)
x = identity_block(x, filters=[64, 64, 256])
x = identity_block(x, filters=[64, 64, 256])

# conv3_x
x = resnet_block(x, filters=[128, 128, 512], s=2)
 = identity_block(x, filters=[128, 128, 512])
x = identity_block(x, filters=[128, 128, 512])
x = identity_block(x, filters=[128, 128, 512])

# conv4_x
x = resnet_block(x, filters=[256, 256, 1024], s=2)
x = identity_block(x, filters=[256, 256, 1024])
x = identity_block(x, filters=[256, 256, 1024])
x = identity_block(x, filters=[256, 256, 1024])
x = identity_block(x, filters=[256, 256, 1024])
x = identity_block(x, filters=[256, 256, 1024])

# conv5_x
x = resnet_block(x, filters=[512, 512, 2048], s=2)
x = identity_block(x, filters=[512, 512, 2048])
x = identity_block(x, filters=[512, 512, 2048])

# 全局平均池化
x = AveragePooling2D(pool_size=(7, 7))(x)

# 输出层
x = Flatten()(x)
output_layer = Dense(units=1000, activation='softmax')(x)

# 构建模型
model = Model(inputs=input_layer, outputs=output_layer)