残差卷积神经网络python

残差卷积神经网络（Residual Convolutional Neural Network）是一种深度学习模型，它在卷积神经网络的基础上引入了残差连接（Residual Connection）的概念。它的目标是解决深层网络训练中的梯度消失和梯度爆炸问题。

以下是一个用Python实现的残差卷积神经网络的示例代码：

import tensorflow as tf

def residual_block(inputs, filters, kernel_size):
    x = tf.keras.layers.Conv2D(filters, kernel_size, activation='relu', padding='same')(inputs)
    x = tf.keras.layers.Conv2D(filters, kernel_size, activation='relu', padding='same')(x)
    x = tf.keras.layers.add([inputs, x])
    return tf.keras.layers.Activation('relu')(x)

def build_resnet(input_shape, num_classes):
    inputs = tf.keras.Input(shape=input_shape)
    x = tf.keras.layers.Conv2D(64, 7, activation='relu', padding='same')(inputs)
    x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)

    x = residual_block(x, 64, 3)
    x = residual_block(x, 64, 3)
    x = residual_block(x, 64, 3)

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

    model = tf.keras.Model(inputs=inputs, outputs=x)
    return model

# 使用示例
input_shape = (32, 32, 3)
num_classes = 10
model = build_resnet(input_shape, num_classes)
model.summary()

以上代码实现了一个简单的残差卷积神经网络模型，包括了一个残差块（residual block）和一个完整的模型构建函数（build_resnet）。你可以根据自己的需求对模型进行修改和扩展。