用def定义一个tensroflow的CBAM模型

好的，以下是使用 TensorFlow 定义一个 CBAM 模型的示例代码：

import tensorflow as tf

def channel_attention(inputs, ratio=8):
    # 获取输入张量的形状
    shape = tf.keras.backend.int_shape(inputs)
    # 计算通道数
    channels = shape[-1]
    # 平均池化
    avg_pool = tf.keras.layers.GlobalAveragePooling2D()(inputs)
    # 全连接层1
    fc1 = tf.keras.layers.Dense(channels // ratio, activation='relu')(avg_pool)
    # 全连接层2
    fc2 = tf.keras.layers.Dense(channels, activation='sigmoid')(fc1)
    # 重塑张量形状
    fc2 = tf.keras.layers.Reshape((1, 1, channels))(fc2)
    # 相乘
    output = tf.keras.layers.multiply([inputs, fc2])
    return output

def spatial_attention(inputs, kernel_size=7):
    # 空间最大池化
    max_pool = tf.keras.layers.MaxPool2D(pool_size=(kernel_size, kernel_size), strides=(1, 1), padding='same')(inputs)
    # 空间平均池化
    avg_pool = tf.keras.layers.AveragePooling2D(pool_size=(kernel_size, kernel_size), strides=(1, 1), padding='same')(inputs)
    # 拼接池化结果
    concat = tf.keras.layers.Concatenate(axis=-1)([max_pool, avg_pool])
    # 全连接层
    output = tf.keras.layers.Conv2D(filters=1, kernel_size=(1, 1), activation='sigmoid', padding='same')(concat)
    return output

def cbam_block(inputs, ratio=8, kernel_size=7):
    # 通道注意力机制
    channel = channel_attention(inputs, ratio)
    # 空间注意力机制
    spatial = spatial_attention(inputs, kernel_size)
    # 相乘
    output = tf.keras.layers.multiply([channel, spatial])
    return output

def cbam_model(input_shape=(224, 224, 3), num_classes=1000):
    # 输入层
    inputs = tf.keras.layers.Input(shape=input_shape)
    # 卷积层1
    x = tf.keras.layers.Conv2D(filters=64, kernel_size=(7, 7), strides=(2, 2), padding='same', activation='relu')(inputs)
    # 池化层1
    x = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
    # CBAM模块1
    x = cbam_block(x, ratio=16, kernel_size=7)
    # 卷积层2
    x = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
    # CBAM模块2
    x = cbam_block(x, ratio=16, kernel_size=7)
    # 池化层2
    x = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
    # 卷积层3
    x = tf.keras.layers.Conv2D(filters=128, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
    # CBAM模块3
    x = cbam_block(x, ratio=16, kernel_size=7)
    # 卷积层4
    x = tf.keras.layers.Conv2D(filters=128, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
    # CBAM模块4
    x = cbam_block(x, ratio=16, kernel_size=7)
    # 池化层3
    x = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
    # 卷积层5
    x = tf.keras.layers.Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
    # CBAM模块5
    x = cbam_block(x, ratio=16, kernel_size=7)
    # 卷积层6
    x = tf.keras.layers.Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
    # CBAM模块6
    x = cbam_block(x, ratio=16, kernel_size=7)
    # 池化层4
    x = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
    # 卷积层7
    x = tf.keras.layers.Conv2D(filters=512, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
    # CBAM模块7
    x = cbam_block(x, ratio=16, kernel_size=7)
    # 卷积层8
    x = tf.keras.layers.Conv2D(filters=512, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)
    # CBAM模块8
    x = cbam_block(x, ratio=16, kernel_size=7)
    # 池化层5
    x = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
    # 全局平均池化
    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    # 全连接层1
    x = tf.keras.layers.Dense(2048, activation='relu')(x)
    # 全连接层2
    x = tf.keras.layers.Dense(2048, activation='relu')(x)
    # 输出层
    outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(x)
    # 定义模型
    model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    return model

以上代码定义了一个简单的 CBAM 模型，包括通道注意力机制和空间注意力机制。在模型中，我们使用了 8 个 CBAM 模块，每个模块包含两个注意力机制。模型输入形状为 (224, 224, 3)，输出为 num_classes 个类别的概率分布。