伪代码 残差卷积神经网络

残差卷积神经网络（Residual Convolutional Neural Network）是一种用于图像分类和目标检测的深度学习模型。它是在传统的卷积神经网络的基础上引入了残差连接的结构。下面是伪代码示例：

输入：图像数据X
输出：分类结果Y

定义残差块函数ResidualBlock(input, filters):
    x = 卷积(input, filters)
    x = 批归一化(x)
    x = ReLU(x)
    x = 卷积(x, filters)
    x = 批归一化(x)
    x = x + input  # 残差连接
    x = ReLU(x)
    返回 x

定义残差网络模型ResNet:
    输入层 = 输入()
    
    # 第一个卷积层
    x = 卷积(输入层, filters=64, kernel_size=7, strides=2, padding='same')
    x = 批归一化(x)
    x = ReLU(x)
    x = 最大池化(x, pool_size=3, strides=2, padding='same')
    
    # 残差块堆叠
    x = 残差块(x, filters=64)
    x = 残差块(x, filters=64)
    x = 残差块(x, filters=64)
    
    x = 平均池化(x, pool_size=7)
    x = 全连接(x, units=1000)
    输出层 = 软最大值(x)
    
    返回 ResNet模型(输入层, 输出层)

# 使用ResNet模型进行训练和预测
resnet = ResNet()
resnet.编译(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
resnet.训练(X_train, Y_train, epochs=10, batch_size=32)
预测结果 = resnet.预测(X_test)