keras搭建的卷积神经网络怎么加入CBAM注意力机制

要在Keras卷积神经网络中加入CBAM注意力机制，需要按照以下步骤进行操作：

1. 导入必要的Keras库和模块，例如：Conv2D、Activation、Add、Lambda等。

2. 定义CBAM注意力机制的两个模块：通道注意力模块和空间注意力模块。

3. 将通道注意力模块和空间注意力模块组合成CBAM模块。

4. 在卷积神经网络的每个卷积层后添加CBAM模块。

下面是一个使用Keras实现CBAM注意力机制的示例代码：

from keras.layers import Conv2D, Activation, Add, Lambda

def channel_attention(input_feature, ratio=8):
    # 计算通道注意力权重
    channel_axis = 1 if K.image_data_format() == "channels_first" else -1
    channel = input_feature._keras_shape[channel_axis]
    shared_layer_one = Dense(channel//ratio,
                             activation='relu',
                             kernel_initializer='he_normal',
                             use_bias=True,
                             bias_initializer='zeros')
    shared_layer_two = Dense(channel,
                             kernel_initializer='he_normal',
                             use_bias=True,
                             bias_initializer='zeros')
    avg_pool = GlobalAveragePooling2D()(input_feature)
    avg_pool = Reshape((1,1,channel))(avg_pool)
    avg_pool = shared_layer_one(avg_pool)
    avg_pool = shared_layer_two(avg_pool)
    max_pool = GlobalMaxPooling2D()(input_feature)
    max_pool = Reshape((1,1,channel))(max_pool)
    max_pool = shared_layer_one(max_pool)
    max_pool = shared_layer_two(max_pool)
    cbam_feature = Add()([avg_pool,max_pool])
    cbam_feature = Activation('sigmoid')(cbam_feature)
    if K.image_data_format() == "channels_first":
        cbam_feature = Permute((3, 1, 2))(cbam_feature)
    return multiply([input_feature, cbam_feature])

def spatial_attention(input_feature):
    # 计算空间注意力权重
    kernel_size = 7
    if K.image_data_format() == "channels_first":
        channel = input_feature._keras_shape[1]
        cbam_feature = Permute((2,3,1))(input_feature)
    else:
        channel = input_feature._keras_shape[-1]
        cbam_feature = input_feature
    avg_pool = Lambda(lambda x: K.mean(x, axis=3, keepdims=True))(cbam_feature)
    max_pool = Lambda(lambda x: K.max(x, axis=3, keepdims=True))(cbam_feature)
    concat = Concatenate(axis=3)([avg_pool, max_pool])
    cbam_feature = Conv2D(filters = 1,
                           kernel_size=kernel_size,
                           strides=1,
                           padding='same',
                           activation='sigmoid',
                           kernel_initializer='he_normal',
                           use_bias=False)(concat)
    if K.image_data_format() == "channels_first":
        cbam_feature = Permute((3, 1, 2))(cbam_feature)
    return multiply([input_feature, cbam_feature])

def cbam_block(cbam_feature,ratio=8):
    # 定义CBAM模块
    cbam_feature = channel_attention(cbam_feature,ratio)
    cbam_feature = spatial_attention(cbam_feature)
    return cbam_feature

可以在需要添加CBAM注意力机制的卷积层后添加CBAM模块，例如：

input_data = Input(shape=(224, 224, 3))
x = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding='same')(input_data)
x = cbam_block(x)
x = Activation('relu')(x)

这样就可以在卷积神经网络中加入CBAM注意力机制。