一维CBAM代码keras

以下是一维CBAM的Keras实现示例代码：

from keras.layers import Input, Dense, Conv1D, Activation, Multiply, Add, GlobalAveragePooling1D, Reshape
from keras.models import Model
import keras.backend as K

def cbam_block(cbam_feature, ratio=8):
    # 通道注意力机制
    channel_feature = GlobalAveragePooling1D()(cbam_feature)
    channel_feature = Reshape((1, cbam_feature.shape[2]))(channel_feature)
    channel_feature = Dense(units=cbam_feature.shape[2] // ratio,
                            activation='relu',
                            kernel_initializer='he_normal',
                            use_bias=True,
                            bias_initializer='zeros')(channel_feature)
    channel_feature = Dense(units=cbam_feature.shape[2],
                            activation='sigmoid',
                            kernel_initializer='he_normal',
                            use_bias=True,
                            bias_initializer='zeros')(channel_feature)
    channel_feature = Reshape((1, cbam_feature.shape[2]))(channel_feature)
    cbam_feature = Multiply()([cbam_feature, channel_feature])

    # 空间注意力机制
    spatial_feature = Conv1D(filters=1,
                            kernel_size=7,
                            padding='same',
                            activation='sigmoid',
                            kernel_initializer='he_normal',
                            use_bias=False)(cbam_feature)
    cbam_feature = Multiply()([cbam_feature, spatial_feature])

    return cbam_feature

inputs = Input(shape=(None, 128)) # 假设输入是长度为128的一维信号
x = Conv1D(filters=64, kernel_size=3, padding='same')(inputs)
x = cbam_block(x)
x = Conv1D(filters=64, kernel_size=3, padding='same')(x)
x = cbam_block(x)
x = Conv1D(filters=128, kernel_size=3, padding='same')(x)
x = cbam_block(x)
outputs = Dense(units=10, activation='softmax')(x) # 假设输出是10个类别的概率

model = Model(inputs=inputs, outputs=outputs)

其中，`cbam_block`函数实现了一维CBAM模块。在该函数中，首先实现了通道注意力机制，计算通道维度上的平均值，然后通过两个全连接层计算出通道注意力权重，最后将该权重与输入特征相乘得到加强后的特征。接着实现了空间注意力机制，通过卷积层计算出每个位置的空间注意力权重，同样将该权重与输入特征相乘得到加强后的特征。最后将通道注意力和空间注意力的结果相乘得到最终的特征。