用python编写卷积神经网络去雾模型

好的，以下是一个简单的卷积神经网络去雾模型的示例：

首先需要导入必要的库：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Input, LeakyReLU, Lambda, Concatenate, MaxPooling2D, UpSampling2D
from tensorflow.keras.models import Model

接下来定义模型：

def build_model():
    # 输入层
    input_layer = Input(shape=(None, None, 3))

    # 第一层卷积
    conv1 = Conv2D(filters=32, kernel_size=(3, 3), padding='same')(input_layer)
    conv1 = LeakyReLU(alpha=0.2)(conv1)

    # 第二层卷积
    conv2 = Conv2D(filters=64, kernel_size=(3, 3), padding='same')(conv1)
    conv2 = LeakyReLU(alpha=0.2)(conv2)

    # 第三层卷积
    conv3 = Conv2D(filters=128, kernel_size=(3, 3), padding='same')(conv2)
    conv3 = LeakyReLU(alpha=0.2)(conv3)

    # 第四层卷积
    conv4 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')(conv3)
    conv4 = LeakyReLU(alpha=0.2)(conv4)

    # 第五层卷积
    conv5 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')(conv4)
    conv5 = LeakyReLU(alpha=0.2)(conv5)

    # 第六层卷积
    conv6 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')(conv5)
    conv6 = LeakyReLU(alpha=0.2)(conv6)

    # 第七层卷积
    conv7 = Conv2D(filters=128, kernel_size=(3, 3), padding='same')(conv6)
    conv7 = LeakyReLU(alpha=0.2)(conv7)

    # 第八层卷积
    conv8 = Conv2D(filters=64, kernel_size=(3, 3), padding='same')(conv7)
    conv8 = LeakyReLU(alpha=0.2)(conv8)

    # 第九层卷积
    conv9 = Conv2D(filters=32, kernel_size=(3, 3), padding='same')(conv8)
    conv9 = LeakyReLU(alpha=0.2)(conv9)

    # 输出层
    output_layer = Conv2D(filters=3, kernel_size=(1, 1), activation='sigmoid', padding='same')(conv9)

    # 定义模型
    model = Model(inputs=input_layer, outputs=output_layer)

    return model

以上模型包含9个卷积层和1个输出层。其中，每个卷积层后面都跟着一个LeakyReLU激活函数。最后的输出层使用sigmoid作为激活函数。

可以使用以下代码来编译模型：

model = build_model()
model.compile(optimizer='adam', loss='mse')

然后你就可以使用该模型来训练和测试了。